With the growing demand for high-quality telecommunication services and the significant increase in data traffic, due to the rising number of applications on the Internet, optical networks emerge as the most suitable technology to supply the required high transmission rates. In this scenario, network operators constantly investigate new solutions to deploy scalable and cost-effective network architectures. This work introduces a comprehensive approach to optimize the cost of installed Optical Transport Network (OTN) interfaces, as well as the required number of wavelengths in the substrate Wavelength Division Multiplexing (WDM) optical network. The strategy involves grooming traffic demands of multiple bit rates on the properly installed distinct-capacity OTN interfaces. A complete Integer Linear Programming (ILP) mathematical formulation is proposed to address the integrated problem of OTN interface deployment, virtual-network formation, traffic grooming on the established distinct-capacity virtual paths, and virtual network embedding on the WDM physical substrate. The main objective of this research is to reduce the Capital Expenditure (CAPEX) of the required deployed OTN interfaces, usually the most costly element in the network, as well as to minimize the required number of wavelengths in the Routing and Wavelength Assignment (RWA) problem, contributing to the overall reduction in network costs and ensuring available capacity for future expansions. As the RWA problem becomes challenging to solve as the network size scales, involving significant computational costs, a multi-step heuristic approach is proposed to efficiently handle larger networks. In this approach, the optimization steps for the OTN design and the RWA solutions are conducted independently. For the OTN-layer planning, the exact ILP formulation (OTN-ILP) is maintained, so that the optimum solution for the cost of installed interfaces on the OTN layer is guaranteed. The RWA problem is proposed through either an ILP formulation (RWA-ILP) or a heuristic approach (BLRW). The RWA-ILP model provides results with the same or slightly superior number of wavelengths compared to the optimum solution of the Integrated ILP formulation, with the cost of providing results in reasonable times only for small networks. On the other hand, the heuristic developed for RWA is able to run any network size, although it requires more wavelengths compared to the RWA-ILP model.

The current scenario in the energy system is driven by the insertion of Distributed Energy Resources (DER), which include Distributed Generation (DG) and Energy Storage Systems (ESS). In this context, a potential alternative for integrating DER with the electrical system are microgrids. The microgrid (MG) is capable of providing better reliability for system operation due to its plug-and-play characteristic, that is, the ability to operate in connected and disconnected modes from the main grid. For the microgrid to be capable to provide support for the integration of DERs, it is essential to have the presence of an energy management system (EMS), responsible for managing the energy resources that is part of the MG. With energy management, it is possible to guarantee the dispatch of each energy resource considering its restrictions, in addition to meeting any request associated with an operational or economic requirement. Aiming to evaluate the performance of the EMS on a MG, this work proposes to evaluate EMS strategies in an MG disconnected from the main grid, composed by a diesel generator, fuel cell (FC), battery energy storage system (BESS) and photovoltaic (PV) generation system. Therefore, the work considers two energy management strategies: state machine and fuzzy logic, implemented using the Matlab/Simulink computational tool. The objective of the strategies is to dispatch each MG energy resource when there is any disturbance associated with variations in generation or load. Furthermore, the strategies aim to operate the FC in the region of best efficiency with the support of BESS. Finally, based on the results operating the FC in the region of best efficiency reduces the consumption of oxygen and hydrogen and improves the voltage profile of the DC bus.

In this work, a methodology focused on identifying anomalies in wind turbines is presented, aiming to anticipate temperature variations in critical machine components. The elements subjected to analysis include the gearbox bearing and the drive-end bearing of the generator. The study is based on modeling and applying machine learning algorithms with the goal of predicting the temperature in these components. The employed regression algorithms encompass linear multiple regression, extreme gradient boosting, and a recurrent neural network called long short-term memory. For modeling these techniques, data from the supervision system of three wind turbines in a Brazilian wind farm consisting of twelve machines are utilized. Machines were chosen with distinct temperature behaviors to assess potential variations in the performance of learning models in the face of diverse thermal behaviors. The machine data undergo a preprocessing stage to identify outliers from the normal operation of wind turbines. Subsequently, the data is divided into specific sets for algorithm application. In the case of the extreme gradient boosting model, a Bayesian optimization technique was employed to find optimal parameters that suit the proposed dataset. The results of the regression algorithms are analyzed in terms of performance metrics, and comparisons between actual and predicted temperatures are conducted within defined control limits, aiming to identify anomalies in the temperature of the studied elements. Finally, the models applied to the three machines are compared for each analyzed component. The main advantages of this model include its ability to provide excellent results for complex prediction problems, low financial implementation costs, and high adaptability for implementation in other machines.

This Thesis presents the proposal and characterization of D-shaped optical fiber sensors using the effects of Dissipative Mode Resonance and Surface Plasmon Resonance using the Attenuated Total Reflection method to measure parameters such as corrosion and refractive index through intensity variation. of the light transmitted after passing through D-Shaped with a single low-attenuation wavelength. The performance of the sensors is obtained through analytical and numerical simulations that are in agreement with each other and with experimental results found in the literature. Of the sensor element modeling, three are analytical, all based on Fresnel equations, and two are numerical, obtained using the Finite Element Method through the COMSOL Multiphysics software. The first analytical modeling considers that light is a ray that has a defined angle of incidence within the optical fiber, the second analytical modeling considers that the light has a normal power distribution within the possible range of angles and the third modeling considers that the power of light is distributed in the optical fiber with a Gaussian profile. The first numerical modeling approximates the light in the optical fiber to a beam with a Gaussian profile traveling through a longitudinal section of D-shaped, while the second modeling uses a cross section of D-Shaped to obtain the refractive index of the effective mode of the light that passes through the D-Shaped. Eleven devices are proposed, four of which are corrosion sensors, three are refractive index sensors and four are cascade combinations of sensors, including combinations that simultaneously measure corrosion and refractive index. Among the corrosion sensors, one uses the surface plasmon resonance effect and three use the dissipative resonance mode, while for the refractive index sensors, one uses the surface plasmon resonance and two use the dissipative resonance. For each sensor, the behavior of unpolarized light and transverse magnetic and transverse electrical polarizations is evaluated separately. The results show that, depending on demand, it is possible to obtain sensors with high sensitivity, wide operating range, with or without resonance. The results highlight the potential of using the resonance effect in a dissipative way, individually or together with the surface plasmon resonance effect.

Breast cancer is one of the main causes of death amongst women. Its mortality ratio is directly proportional to the stage in which the tumor is detected. If detected in its early stage, the 5 years survival rate is above 90\%, making early detection of upmost importance. Currently X-Ray mammography is considered the gold standard for diagnosis. Despite being the most applied technique, X-Ray mammography has some drawbacks, like patient discomfort, high false positives and high false negatives rate, and high exposition to ionizing radiation. Due to the limitations of current techniques, microwave imaging has risen as a complementary imaging modality due to its high sensibility to cancer diagnoses due to the contrast between dielectric properties of heath tissue and tumorous tissue in the microwave region. In this work we present a new tomography system where robotic arms are used to position the antennas around the breast using information obtained from a depth camera, resulting in bigger flexibility and patient comfort. The system was tested using breast phantoms and was capable of detecting and locating the tumor. Beyond that it was demonstrated the possibility of mobile usage utilizing low-cost single board computers; the possibility of utilizing cloud computing to offset computationally heavy tasks; the possibility of the utilization of real-time imaging for decision making in regard to antenna positioning.

In this work, techniques that allow the detection of pores in fingerprint images are presented and evaluated. The first evaluation takes place in relation to the techniques that use filtering tools for the pore extraction and detection process. The second, in turn, concerns the use of convolutional neural networks (CNN), with a focus on machine learning. After comparing the existing techniques, the neural network topology proposed by (ALI; WANG; AHMAD, 2021) was implemented. The choice was motivated by the fact that the method in question guarantees the best results compared to the others, taking into account the metrics called true detection rate (RT) and false detection rate (RF). Such metrics are used to verify the success and error rate of the network when detecting a set of pores in the fingerprint image, respectively. As you can imagine, the network will be better the higher the hit rate and the lower the error rate. A contribution that is also significant in this work, and that deserves to be highlighted, is the use of a database, which, as far as is known in the researched literature, has never been used. This database is composed of real images obtained through forensic photography on images of fin- gerprint traces found at a crime scene. In addition, this master’s thesis innovated in the sense of using images with a resolution of 96 dpi, which is much lower than the resolution of 800 dpi established as a minimum for this pore detection process. To finalize the contributions, we must also talk about the implementation of Grid Search as a tool for choosing and optimizing neural network hyperparameters. The results obtained after the previously described implementations reveal that, initially, the rates of RTand RFwere not very satisfactory, reaching values of RF= 77,9% and RT= 56,5%, respectively. However, when the window analysis was performed, which corresponds to the restriction of the analysis on subimages of the main image, it was verified that, for a window of size 500 × 200, the algorithm presented satisfactory results, reaching a RTof 80,8% and an RFof 4,5%, respectively. In the end, the use of the method proposed in this work proved to be efficient in the process of detecting pores in images of traces of fingerprints found at a crime scene, a fact that contributes significantly in the field of Forensic Sciences, since with the marking from a minimum of 20 pores it is possible to individualize a person. 

In this work, a study is presented regarding the vulnerabilities of ADS-B communication (Automatic Dependent Surveillance – Broadcast), considering above all the risks to operational security. This technology was developed to transmit surveillance information, that is, data related to position, altimetry and speed, from the avionics of the aircraft itself, being an evolution to radars. An encryption solution is developed for this communication using format-preserving encryption, also assessing the use of symmetric block ciphers used in a lightweight environment, such as the Internet of Things, in order to obtain better computational performance. in solutions with embedded systems. It is observed that such ciphers, when used as a pseudorandom function, maintain a high entropy value with low computational cost, meeting the desired security levels. This study is related to similar research with the objective of confronting the different approaches and complementing the studies. Finally, the computational performance obtained with the proposed solution is analyzed by processing real data from aircraft in the landing and takeoff phase at the International Airport of Recife/Guararapes - Gilberto Freyre, based in Recife/PE.

The connection of transmission lines overhead ground wires to the grounding  grids of substations represents a division path for the current injected by the measurement  equipment during grounding resistance measurements. The solution in these cases would be  to disconnect the wires to perform the measurement. However, this is not always possible,  leading to the need to adopt other strategies, such as using high-frequency earth meters.  These earth meters promote the decoupling of the ground wires due to the increased  impedance associated with high frequency. In this regard, the frequency of 25 kHz emerges  as a possible solution for use in substation measurements. However, there are still few  studies that evaluate the effects of this frequency in this application. Furthermore, there are  no regulatory standards that establish criteria for the use of high-frequency equipment in  these installations. This master thesis is inserted in this context, presenting an evaluation of  some of the effects that the use of high frequency can have on arrangements typically used  in substation grounding resistance measurements. A study was conducted to assess the  response of ground grids to currents of 100 Hz and 25 kHz in terms of their impedance, as  well as the occurrence of coupling between the voltage-current circuits and the current that flows through the ground wires. The simulations considered grids of varying dimensions and  soils with a wide range of resistivity. The analyses indicated that there are conditions in  which the results can present significant differences.

Realizing a study on the graduation and selectivity of an electrical system's protection, ensuring its coordination, is an essential task to ensure continuous load service and mitigate potential harm to equipment and individuals. However, this study demands a significant time from engineering firms, particularly during the data acquisition phase required for the graduation process and the subsequent coordination phase.

The coordination procedure entails a meticulous analysis of pertinent relay pairs, categorizing them as either primary safeguards or secondary measures for each fault scenario. At this juncture, and to the determination of protection relay settings, it becomes imperative for the engineer to be equipped with current data inherent to the scrutinized system, accounting for various fault types and diverse contingencies. To accomplish the acquisition of these short-circuit current values for a specific system, encompassing contributions from adjoining equipment, a Python program has been devised to interface with ANAFAS, software widely employed within the Brazilian electrical system. This program, in conjunction with a innovative computational tool for automated protection coordination of overcurrent relays, regardless of their directional settings, developed in VBA, significantly streamlines the time required for conducting a graduation study.

The automatic coordination program, which stands out positively compared to other existing methods, employs optimization techniques and minimizes the operating time of relays closest to the fault while maintaining selectivity and sensitivity criteria. After the code execution, the data is presented through reports, providing information on the operating times for the presented short circuits, as well as the coordination charts of the protections.

This work proposes a new approach in the exploration of zinc oxide (ZnO) nanoparticles(NPs) to improve the performance of third-generation solar cells, with a focus on organic solarcells (OSCs) and perovskite solar cells (PSCs). ZnO NPs dispersed on the top surface of thedevice can act as nanodiffusers, reducing its reflection and increasing the absorption of solarradiation in the photovoltaic active layer, by introducing non-orthogonal optical paths in thedevice (light pathway enhancement). The contribution of ZnOnanodiffusers in improving solarcells performance was analyzed through computer simulations, based on finite element method(FEM) as well as experimentally. ZnO nanoparticles with size of about 160 nm were synthesizedby a green synthesis route and characterized. Thin films formed by deposition of suspensionof ZnO NPs on the transparent indium tin oxide (ITO) electrode, by drop casting and spincoating methods, having its optical response evaluated. In this work we propose methodologiesfor optical and electrical modeling of third generation photovoltaic cells with the applicationof ZnO NPs at the top of the device. The results of the optical modeling indicate that ZnOnanospheres with a diameter of 160 nm present a high average Albedo value (0.88) in the visiblespectrum range, and scatter solar radiation predominantly in the forward direction. The use ofZnO NPs (160 nm in diameter) on the surface of an OSC can reduce the device’s reflectance byup to 95 % for λ = 530 nm, promoting an efficient light coupling in the cell’s active layer andsimultaneously increasing the absorbance (26 %) of the device. Light trapping and anti-reflectiveeffect contributions improve the generation of photocurrent, showing an increase in short-circuitcurrent density of 27.9 % for OSCs. For PSCs, an increase in absorbance of 32 % and a reductionin reflectance of 96 % was observed for λ = 530 nm. This work describes the manufacturingprocess of third-generation solar cells, both OSCs and PSCs. The optical gain generated byZnO nanostructures resulting from both, the anti-reflective effect and light scattering, has beendemonstrated with substrates containing the active material of the devices. With the depositionof ZnO NPs on PSCs, it was verified experimentally that the use of ZnO NPs deposited on top,yields in an enhancement in the efficiency of the photovoltaic device of 23.5 %.regime. 

Due to the rapid expansion of photovoltaic systems in the last decade, the need to locate the Maximum Power Point (MPP) to maximize the captured solar energy under various environmental conditions has become clear. When all modules in a photovoltaic array receive the same solar irradiance, traditional Maximum Power Point Tracking (MPPT) techniques can easily find the MPP. However, if the irradiance levels on the PV modules are not uniform, finding the global maximum power point Global Maximum Power Point - GMPP for these algorithms becomes infeasible, since these techniques can be left operating at a local maximum as long as shading remains. A partial shading, which can occur from shadows, clouds, buildings or trees, for example, exhibits in the power-voltage curve a behavior with multiple peaks, since the modules are protected by bypass diodes. In this context, in this paper two techniques are proposed that extract the GMPP from a power-voltage curve with multiple peaks. These techniques are compared to recent techniques in the literature where comparative simulation and experimental results are obtained and flowcharts are presented. It is worth noting that model-based techniques reduce energy losses because they have faster dynamic responses compared to heuristic techniques. Therefore, two model-based techniques are proposed, only one of them is valid only for photovoltaic string. One of these algorithms uses the two-diode model from the photovoltaic parameters estimated in an offline manner using Hybrid Pattern Search Technique (TH-PS). The other algorithm uses a diode model. As both techniques are model based, an irradiance microestimator has been proposed, whose purpose is to estimate the irradiance of each module at a low cost. This microestimator can also serve as monitoring of a photovoltaic (PV) plant, since it can send the irradiance information to a central computer via Wi-Fi, since it uses an ESP8266 for communication. Finally, after modeling the converter used for MPPT conditions, the dynamic results of simulations, using MATLAB software, and experimental results, using an experimental bench, for various climatic conditions are provided.

Anchor rods are used to support guyed cables from electrical transmission towers. They are part of a complex anchoring system that is assembled to support the massive structure capable of carrying energy to several regions. Often the installation of these towers takes place in a remote region, which makes their maintenance challenging. Furthermore, checking the integrity of the anchor rods, which are driven into the ground, is carried out through a visual inspection. This requires the ground around the rod to be excavated, which makes the process expensive and dangerous for workers. Several non-destructive methods have been proposed to verify the integrity of anchor rods, even buried and without the need to dig the ground. One of them is based on frequency domain reflectometry. Electromagnetic signals are injected by a vector network analyzer into a transmission line composed of an anchor rod. The signal reflection levels along the rod are analyzed. This analysis can provide valuable information regarding the integrity of the rod, since impedance changes can be caused by corrosive processes on its surface. However, it is very common for auxiliary structures to be present along the anchor rod. They can influence the diagnosis of the state of the anchor rod. In an innovative way, this work proposes a new methodology to decrease the undesired effects of auxiliary structures on the reflectometry analysis. Thus, here, measurements and simulations are made on anchor rods to identify possible failures in their structure through the reflectometry analysis, which is optimized by reducing undesired effects.

The increasing demand for versatile detection solutions and improved measurement performance in challenging operational environments has driven extensive research in fiber optic sensors to achieve enhanced operations. A promising approach to enhancing detection systems involves considering specific aspects, such as expanding the operating range and simultaneously measuring multiple parameters, especially when these parameters are interconnected, as is the case with temperature and corrosion or temperature and refractive index. This doctoral thesis presents the enhancement of functionalities of a distributed temperature sensor based on Raman scattering (DTS-R) in optical fibers and experimentally proposes the development of a new multiparameter sensor based on time-domain anti-Stokes Raman reflectometry. The enhancement of a distributed temperature detection system through Raman scattering in optical fibers is performed, focusing on the temperature operating range. This system utilizes a commercial optical time-domain reflectometry (OTDR) equipment and amplification through erbium-doped fiber amplifiers (EDFA). The sensor's performance is experimentally evaluated over a wide temperature range, spanning from -196.15 °C to 400 °C, and over long distances, up to 27 km. Analyses are conducted using a special type of fiber with a metallic coating for high-temperature operation. The results provide information about the sensor's sensitivity, accuracy, and resolution across the wide temperature range. Additionally, the development of a new multiparameter sensor combining temperature, corrosion, and refractive index detection functionalities is presented. This sensor incorporates sensor heads designed with different optical fiber structures into the traditional DT-R for corrosion and refractive index monitoring. The sensors are tested in experiments to evaluate their response to different refractive indices, temperatures, and a controlled corrosion process, enabling the determination of the corrosion rate. The results demonstrate that the multiparameter sensor is capable of simultaneously and independently monitoring corrosion and refractive index along multiple points of the fiber link, as well as temperature in a distributed manner. This approach not only adds new functionalities to DT-R but also enhances measurement results for the analyzed parameters. The sensor's sensitivity is 0.5 dB/10-3 UIR for refractive index variations in the range of 1.3370-1.3840 UIR and 0.74 dB/nm for the detection of 100 nm aluminum corrosion deposited on the cleaved end of an optical fiber. The possible applications of the sensors in various sectors are mentioned, with emphasis on the oil and gas industry, where these sensors can contribute to enhancing the monitoring and maintenance of critical infrastructures. By integrating simultaneous measurement of multiple parameters in a single sensor, this approach offers a versatile and effective solution to meet measurement needs in challenging operational environments.

An Instantaneous Frequency Measurement (IFM) system consists of a set of elements capable of processing an input signal using discriminators to identify the sub-band to which the signal's frequency belongs. The displayed results are represented by binary values that reflect the output power of the discriminators. Therefore, it is of utmost importance to appropriately select the frequency response of these elements to ensure the proper functioning of the system. By designing the discriminators correctly, direct scanning can be achieved, eliminating the need for post-processing. In this thesis, evidence is presented that choosing a balanced binary code (BBC) to represent the output bits brings significant advantages in the design of frequency discriminators. The designed discriminators are composed of a set of four Frequency Selective Surfaces (FSS) based on the fractal geometry Sierpinski Curve, with different iterations, thus replacing traditional frequency discriminators such as interferometers and filters. As an application of the designed discriminators, a 4-bit IFM system will be hypothesized with a design resolution of 323.12 MHz for each of the 16 sub-bands distributed in the frequency range between 0.39 GHz and 5.56 GHz. The results of computational simulations performed in CST Microwave Studio, along with measurements using a network analyzer, will be presented and appropriately compared. Both in measurements and in simulations, the FSS is placed between the antennas. The parameter analyzed in the simulations and measurements is the transmission coefficient, 𝑆₂₁, between two horn-type antennas used in the measurements and represented by ports 1 and 2 in the computational simulations.

The increase in life expectancy worldwide and the decline in birth rates have contributed to an increase in the aging of the population. This phenomenon, coupled with the rise of chronic diseases, poses growing challenges to healthcare systems in caring for the elderly to preserve their quality of life. Advances in health and technology have enabled remote monitoring of vital information, assistance in managing daily medications, and the prevention of serious accidents. In this context, technologies like Ambient Assisted Living (AAL) emerge as solutions to monitor the health, emotional state, and daily activities of the elderly, providing them with independence, safety, and well-being. To ensure that these solutions meet the needs of this audience, it is crucial to standardize the perceptions of usability and acceptability of AAL systems. During the development of such systems, it is essential to properly specify the non-functional requirements that impact usability and acceptability, considering legal requirements and stakeholders' experiences, as these aspects are relevant for this type of system. This thesis presents the NFR-driven development (NDD), a framework that assists in specifying non-functional usability and acceptability requirements for AAL systems, based on legal requirements and stakeholders' experiences. To support this proposal, a Systematic Literature Mapping (SLM) was conducted to identify the main non-functional requirements for AAL systems, including legal requirements that may impact the development of these systems. From the results obtained with the SLR, the non-functional requirements influencing stakeholders' usability and acceptability were identified, as well as the legal requirements relevant to this type of system. The NDD framework brings together relevant concepts and provides guidance to requirements engineers and developers during the process of specifying non-functional requirements, considering legal requirements and stakeholders experiences. By adopting this framework, it is expected to achieve AAL systems that effectively meet the needs and expectations of stakeholders, ensuring compliance with legal requirements.

From when it was used in ancient vessels for geographic orientation, to today where it is used to treat cancers and tumors, the use of the magnetic field has brought technological evolution both in the construction of devices and in the most different procedures, and for its proper application, nominal monitoring of the magnetic field is essential. In this sense, this thesis project presents computational modeling and experimental studies for the construction of fiber optic sensors and devices, which use the magneto-optical Faraday and Kerr effects. The sensors proposed in this thesis are capable of monitoring the magnetic field with varying sensitivity and operating range, in which one of the configurations can reach an operating range of 0 to 200 mT. Due to specific applications, where there are temperature variations, a study was also carried out on how temperature can affect signal modulation, as well as ways to mitigate its influence. The sensitivity results obtained are comparable with those found in the literature, with one of the simulated structures achieving a resolution of 0.051 mT. The studies carried out aim to design and characterize the sensor structure so that its signal is optimized depending on its application. It is worth highlighting that the experimentally constructed system also serves the purpose of characterizing magnetic materials, and obtaining magneto-optical parameters from these materials. Finally, this thesis also proposes a device to control the polarization of an electromagnetic wave, based on the magneto-optical Faraday Effect and absorption in a metallized D-shape fiber. The device can be electronically controlled by solenoids, which allows for better performance when compared to mechanical controllers. Finally, the simulation results show that the device can polarize and control the polarization of light with a maximum insertion loss of 5.71 dB for an unpolarized wave and a minimum of 2.61 dB for an already polarized wave.

Random laser (RLS) emerges with great boost in photonic exploration for complex systems studies and biological systems. This thesis addresses the construction, characterization and application of RLS of different gain and dispersion materials, aiming at its use in the optical transillumination system of biological tissues. In addition to being studied random laser emission intensity fluctuations, observing the existence of non-Gaussian statistics of the type Lévy, distribution of Izrailev and breaking replica symmetry using 6G Rhodamine RL with nanoparticles of . In this thesis the low coherence issuance of the RLs were explored to generate images free of biological tissue blemishes. In the first chapter of this text an introduction to RLs and its applications in the area of biomedical optics is made. The second chapter focuses on the theoretical description of the emission of colloidal and solid state RLs. The third chapter presents the construction and characterization of RLs, with the observation of nonlinear effects and photonic phase transitions on RLs systems. A large set of RL emission spectra were analyzed for statistical analysis of radiation. In the fourth chapter, the use of RL is presented in the transillumination image technique for generation characterization of biological systems. In this case, an organic Pyridine2 dye was used in ethylene glycol with nanoparticles,  to generate RL with emission in the first biological window (750nm). Experimentally evaluated the coherence of RL emission, with the quantification of laser radiation coherence length (). The use of RL was first demonstrated in the optical transitional technique for dental tissue image generation. The results indicate that Pyridine2 RL is a source of great potential to generate images of spot free dental fabric in the nearby infrared.

The usage of machine learning (ML) techniques in different academic and professional fields confirms its theoretical and practical utility. The communications field is no exception, in fact, models that learn from data were already in use prior to the recent advancement in the ML field. This work investigates different kinds of usage that can be done with ML models in three different problems, seeking to show their high flexibility and present alternative ways of obtaining classical results which employ well established algorithms, or even outperform them in some scenarios. The first problem discusses the so-called Markov-Gaussian channels and compares a ML model with the already common hidden Markov models approach. The second problem deals with non-orthogonal multiple access transmissions and compares a ML model with the usually employed decoding algorithm. The third presents a chaos-based communication system and compares the maximum likelihood decoding to a neural network-based one.

This work deals with the evaluation of non-orthogonal waveforms of the type Spectrally Efficient Frequency Division Multiplexing (SEFDM), in which it is possible to increase the spectral efficiency compared to Orthogonal Frequency Division Multiplexing (OFDM) systems by compressing the spacing between subcarriers below the orthogonality limit. The break of the orthogonality causes the appearance of Inter-Carrier Interference (ICI), which becomes more intense as the spectrum compression increases. The performance of SEFDM was compared with OFDM, which is the waveform used in 5G NR, both in simulations and experimentally in the context of Sixth Generation of mobile communications 6G. During the simulations, the systems were analyzed for Bit Error Rate (BER) with and without the application of error correcting codes of the type Low Density Parity Check (LDPC), for spectral efficiency and Peak-to-Average power Ratio (PAPR). The experiments were carried out considering two scenarios in the context of 5G and beyond, namely the wireless channel and Radio Over Fiber RoF. The transmitters and receivers were implemented in GNURadio, an open-source platform to work with Software Defined Radios SDR and through it was possible to carry out the transmissions and receive the signals.

Na presente dissertação realizamos a investigação experimental de um sensor à fibra óptica com estrutura heteronúcleo, tendo a finalidade de avaliar a viabilidade deste como possível candidato para o monitoramento do estado de carga de baterias de chumbo-ácido. A estrutura utilizada é formada por uma fibra óptica monomodo (SMF - Single Mode Fiber) unida com uma fibra multimodo (MMF - Multi-Mode Fiber) em cada uma de suas extremidades, formando um arranjo multimodo-monomodo-multimodo (MSM), onde a fibra SMF é a parte sensora da estrutura. Com intuito de se obter um sensor compacto e de boa sensibilidade, foram realizados experimentos com comprimentos distintos de SMF, de tal modo que fosse possível selecionar o mais adequado para a aplicação proposta. As medições são realizadas variando o índice de refração externo à SMF, primeiramente com amostras de glicerina diluída em água, para caracterização do sensor, e, por fim, com amostras de eletrólito de bateria de chumbo-ácido com diferentes concentrações. O sensor final consiste no uso de uma fibra óptica SMF com comprimento de 30 mm, e a resolução obtida é de 1,85 g/L do eletrólito, de modo que o sensor é capaz de detectar diferentes níveis de carga em diferentes tipos de baterias de chumbo-ácido.

The anomaly detection consists of identifying patterns that differ from an expected behavior. It is an important field of study, whose applicability extends to several domains, such as the security of communication networks, the detection of diseases and frauds, among others. The anomaly detection is an essential step in decision-making processes, such as planning the factory maintenance or starting the treatment of serious illnesses. Anomalous behaviors can be caused by errors in the process or by events not known by the detection system. Anomaly detection presents some challenges that makes it different from a traditional classification problem. One of them concerns the unbalance of the data available to train the anomaly detection model. The lack, or even the availability in small quantities of data belonging to the anomalous classes are quite common situations in real-world problems. It makes difficult to define a region in space that contains all possible normal behaviors without comprising the anomalies. Several techniques have been developed over the years to address this problem. However, a group of techniques has gained special attention from the academic community. Such techniques are based on the use of deep learning. They consist of processing information across multiple layers, making it possible to obtain more meaningful representations of the input information for a given classification or regression problem. Despite the advances achieved in this field, using deep learning in anomaly detection tasks still presents some difficulties, especially in obtaining characteristics capable of representing the normal class in a satisfactory way, while simultaneously distinguishing it from the anomalous class. This paper presents the development stages of an anomaly detection system based on the joint operation of deep and traditional machine learning techniques. The first approaches that we analyzed consisted of supervised trained algorithms, assuming that all anomalous classes were known, to promote a better understanding of the problem. Then, we proceeded to the approaches in which the training of the algorithm was performed only by using data belonging to the normal class. Among the techniques proposed in the thesis, the one that presented the most promising results regarded the training of the feature extractor together with a prototype selection process. The technique presented relatively high and stable mean AUC values, e.g., above 0.95, for a niche of anomaly detection problems. The trained models were evaluated with databases composed of sound and vibration signal spectrograms, collected by sensors placed on electromechanical devices.

In this work, a fault ride through control scheme for inverters that interface distributed generation sources with a power grid that contains conventional synchronous generators is developed and implemented. With the proposed control strategy, the inverter performs its usual tasks when the grid is under normal operating conditions. However, when the grid experiences a fault, the inverters start to operate so that the active powers delivered by the conventional synchronous generators are maintained as close as possible to the pre-fault values, in order to reduce the machines acceleration and system frequency oscillations. Simulations of the proposed control scheme are performed in MATLAB, considering large PV plants connected to the IEEE 9 bus test system. The proposed control strategy is further evaluated through experimental results performed in a prototype consisting of a synchronous generator and a distributed generation converter connected to the utility power grid. The results obtained show that, in comparison with using the grid codes requisites of several countries, the application of the proposed control strategy leads to the best transient stability performance.

The Daily Operation Scheduling (DOS) gets new challenges while renewable energy is inserted into the power system. In addition to the operation, the variability of the power available by these sources also causes some problems due to system operating constraints, such as the Power Curtailment (or Constrained-off), and the hourly pricing variability, represented here by Locational Marginal Pricing (LMP). Therefore, available energy shifting applications, through the use of Battery Energy Storage System (BESS), can offer greater efficiency to the system, minimizing the negative effects caused by power variability. In this sense, this work shows a proposal to analyse the behaviour of the energy operation cost, the LMP and the Power Curtailment from the large-scale penetration of renewable energy sources with the presence of BESS. The methodology used integrates the solution of the DOS problem with a risk analysis based on different scenarios of wind and solar photovoltaic power generation. Moreover, these two sources are modelled to be dispatched, not mandatory, to be able to observe the Power Curtailment, and the impact of distributed generation on the expected load curve for the system is also considered. The DOS problem is formulated through the DC Optimal Power Flow and is solved by the Predictor-Corrector Interior Point method. This solution is part of the iterative algorithm used to couple the DOS problem with LMP, where electrical system losses and a fictitious nodal demand model are considered. Lastly, the analysis is performed using the adapted IEEE RTS 24-bus system with the generation, demand and distributed generation data from the Northeast subsystem. The simulation results are presented and discussed, demonstrating the effectiveness of the analysis and use of BESS to mitigate the total cost of the daily energy operation and the Power Curtailment.

Quality control is a critical aspect, especially in Industry 4.0 context. In addition to being extremely important to meet the functional prerequisites of a given product, quality is closely related to safety, security, and economic issues. In this work, we address two specific aspects of quality certification in the modern sense of the industry: detection of anomalies in embedded systems and classification of chemical or biological substances. The solutions addressed are based on machine learning models.

In the electronics industry, component miniaturization and the use of multi-layer boards have considerably increased the complexity of testing. Consequently, traditional forms of testing based on manual inspections have become outdated and inefficient. In addition, Industry 4.0 demands products with a high level of personalization. This imposes additional requirements, such as a high degree of flexibility in the conception, design and testing processes. Therefore, effective and flexible solutions that do not require physical contact with the tested product are needed. Our study features automated and non-invasive testing solutions.

Regarding substance testing, spectroscopy techniques are traditionally employed using spectrometers. Despite of being a very mature technique, its limitation is the cost and complexity of the equipment. We propose a simple, however efficient alternative to perform tests without the use of spectrometers.

For validation, we designed and built prototypes to carry out experiments. For anomaly detection, we built a system using development board. Six software versions have been implemented – one of which is functional, while the rest represents some kind of anomaly. Anomalies are detected by analyzing temporal signals captured from the circuit in a non-invasive way. The signals are converted into spectrographic images that are analyzed by a machine learning model, trained to detect whether the circuit behaves as specified.

For classification of substances, we present a Proof of Concept. Instead of employing a spectrometer, we use an optical transmission and reception system. The transmitted signal has a specially designed waveform for maximum performance. The detected signal is converted into a spectrographic image that is used by a machine learning model that performs the classification.

Experimental results demonstrate the effectiveness of the proposed test methods. For various electronic system test scenarios, the detection rate reaches 100%. Substance classification also performs optimally (100% accuracy) in various configurations. Additionally, we present a technique to increase performance by transforming the data used for training and validating the model. The effectiveness of the technique is experimentally proven both for detecting anomalies and for classifying substances.

Optical networks appear as one of the main infrastructure for transporting large amounts of data in current times. Among most studied optical technologies investigated in recent works we can mention Elastic optical networks, which show outstanding use of spectrum due to the possibility of working with variable-bandwidth requests. The present work deals with the use of heuristics and meta-heuristics for improvements in routing and spectrum assignment in Elastic optical networks. A routing and spectrum assignment scheme based on Split Spectrum for single and multiple paths is initially proposed, which is capable of increasing network availability by reducing the loss of options for inserting requests in the route frequency spectrum, with the consequent mitigation of blocking probability of next requests. It is also proposed a meta-heuristic based on Particle Swarm Optimization (PSO) with the objective of defining solutions for the spectrum assignment in Elastic optical networks, using the algorithm known as MSCL (Min Slot-Continuity Capacity Loss) and an optimization methodology by series of functions. This scheme takes into account the peculiarities of each route in the network, grouping common characteristics so that it can be used to obtain matrices with optimized input parameters, in a more adequate way for the capacity loss calculation by the algorithm, also modifying the way of calculating the cost of allocation in such networks, observing the possibilities of improvements with the use of optimization algorithms.

Pesquisadores do LSI-UFPE vem desenvolvendo um módulo eletro-óptico que é parte de um sistema sensor capaz de detectar a presença de cinco diferentes tipos de gases dissolvidos em óleo de transformador. A presença desses gases é relacionada com a existência de potenciais falhas. Normas internacionais estipulam as concentrações aceitáveis para indicar uma operação normal do transformador. Para a detecção do gás o sistema sensor em desenvolvimento no LSI usa uma técnica conhecida como WMS –– Espectroscopia de Modulação de Comprimento de Onda. Ela é necessária devido às concentrações diminutas de gases que deve ser detectada. Um dispositivo fundamental para a aplicação desta técnica é o Amplificador Lock-in. Os amplificadores lock-in comerciais apresentam um custo elevado e geralmente possuem muito mais funcionalidades do que o necessário para o sistema sensor. Sendo assim, esse trabalho tem como foco o desenvolvimento de um amplificador lock-in digital utilizando microcontroladores comerciais de baixo custo e que sejam de fácil integração ao sistema sensor em desenvolvimento.

With the rapid development of digital signal processing tools, image contents can be easily manipulated or maliciously tampered. Fragile watermarking has been largely used for content authentication purpose. This dissertation presents new proposals for image fragile watermarking algorithms for tamper detection and image recovery. The watermarked bits are obtained from the parity bits of an error-correcting code whose message is formed from a binary chaotic sequence and from bits of the original image. In the first proposed algorithm, the watermarked bits are inserted in the frequency domain using the Discrete Wavelet Transform. The imperceptibility, detection, and recovery of this algorithm are tested for various attacks used in signal processing. In the second method, the watermarks bits are embedded using the least significant bit method. A comparison between the proposed algorithms shows that the former exhibits greater imperceptibility, while the latter exhibits better recover capability. The proposed algorithms are analyzed both for grayscale and color images. Comparison results reveal that the proposed technique perform better than some existing methods.  

A microgrid is defined by the set of distributed generation units (GD), storage units and loads, which connected in parallel form a small electrical power system. The microgrid can operate connected to the utility grid, where the loads are fed through GD units and the power deficit is provided by the grid. In this operating mode, the voltage and frequency reference is defined by the network. Another possible scenario is when the utility grid is not available and the microgrid must operate autonomously (isolated), in this situation the power flow between the GD units and the loads must be balanced, that is, the power supplied by each GD unit must be proportional to its nominal capacity.  In this work, a comparative study is carried out between the droop control strategy and a new control technique called master-slave without communication, in microgrids operating in autonomous mode. In order to represent the dynamic behavior of microgrids, in this work a small-signal mathematical model of the master-slave without communication microgrid is proposed and the small-signal mathematical model of the droop microgrid is developed. From the mathematical model of both microgrids, the stable region in the complex plane is compared for different values of the droop coefficients. The performance of control techniques in power sharing is evaluated through a set of merit figures. Finally, the performance of both microgrids is evaluated under non-linear and unbalanced load operating conditions, and an unbalanced voltage compensation strategy is implemented in the master-slave without communication microgrid.

Vital signs monitoring is one of the most recommended strategies by health professionals for control, patient’s triage and prognosis assistance of chronic or infectious diseases. The remote mass acquisition of physiological parameters of patients in clinical or residential environments, when efficiently managed, can provide valuable information about trends in epidemiological outbreaks in district, municipal, state, national or international regions. In this way, the main objective of this work is to use scientific consolidated technologies and methodologies to make the vital signs monitoring accessible to the user with only a smartphone in an effective, low-cost and easy way to use. This work presents the development of low-power wearable devices enabled with the NFC (Near Field Communication) technology, in a bracelet format, capable of simultaneously measuring heart rate, temperature, blood oxygen saturation and blood pressure in a non-invasive, comfortable and real-time way. Through a comfortable analog system for arm-wrist ECG acquisition, the device is capable of perform cuff-less measurement of blood pressure through PAT (Pulse Arrival Time) and heart rate parameters. A dynamic NFC interface was also developed, with simultaneous wireless transfer of data and energy between the passive prototype and a smartphone. Moreover, a graphical user interface and an online data log were developed for simultaneous monitoring of four vital signs, through a smartphone and the semi-passive prototype. A data collection protocol was developed with volunteers with approval of the ethics committee for studies with human beings (CAAEs: 24617219.4.0000.5208 and 45209121.2.0000.5208). Such data were used to validate the vital signs acquired by the prototypes with calibrated commercial equipment and evaluate the usability of the developed system by the volunteers. In accordance with clinical and scientific standards of reliability, validations were carried out through statistical analysis of the differences between the data acquired by the prototypes and the commercial equipment. Among the results, there were measurements of vital signs within the recommended clinical standards after prototypes optimization, whose averages and standard deviations of the acquired errors were e_BPM=0.0±1.22, e_SpO2=0.2±0.63%, e_Temperature=0.4±5.1 ^oC, e_SBP=0.3 ±5.1 mmHg, e_DBP=0.7±4.23 mmHg.  In addition, the proposed system was highly acceptable and well evaluated by the interviewed volunteers.

Due to the particularity existing in the various domains of Embedded Systems, it is often difficult to understand the requirements that must be identified and translate them into the system's expected functions and limitations. The inherent complexity of different domains and the need to find defects or flaws quickly and accurately are essential factors for developing these systems. Despite advances in the development of embedded systems, it is still an area that deserves attention from researchers due to its complexity and the few requirements engineering initiatives that meet these systems' particularities. Every day some approaches to the development of Embedded Systems are proposed, considering several knowledge areas, such as ontology. In this context, this thesis aims, based on the Semantic Web's open standards (the formalism of descriptive logic and the Ontology Web Language), to define an ontological approach to assist the specification of requirements in Embedded Systems. Thus, through the built Ontologies, we highlight simulations of requirements specifications in real scenarios, extracting the requirements used, standards, and the positive and negative impacts that they can cause among themselves. The research method used was a systematic mapping of the literature, a systematic review of the literature, a survey, and interviews with experts in the field. As contributions of this thesis, we note the progress in using logically formalized ontologies for the unambiguous and shared representation of requirements knowledge in Embedded Systems. This is expected to assist developers of embedded systems, creating a common core of requirements. It is essential to boost the conduct of research in requirements engineering to have quality and precision in the specification in the development of embedded systems.

A modelagem de uma curva de potência é um fator crucial para o desempenho e operação de uma fonte eólica, contribuindo desde o controle até a melhora no desempenho de uma turbina ou parque. Atualmente existem várias técnicas para o ajuste deste fator, cujos métodos utilizados são conhecidos como paramétricos e não paramétricos. Este último cria uma heurística própria dependendo de uma característica ou tendência específica de como os dados de (ventos x Potência) de um parque ou turbina se comportam. Este Projeto de Tese visa demonstrar que é possível aumentar a precisão da previsão de geração eólica em curto prazo com a utilização de algumas técnicas originais que utilizam a inferência pré-estabelecida em sistemas Fuzzy e técnicas de Deep Learning como redes de aprendizagem profunda inicial. Sendo necessário para isso, aplicar estas técnicas para a melhoria da previsão, na modelagem da tendência base de um sistema eólico, a sua Curva de potência, contribuindo para a melhora notável do desempenho das previsões em modelos de geração eólica no curto prazo.

This work presents development of a microwave device of a fault detection system on anchor rod, which are structures that help support the guyed power transmission towers. This thesis project presents a system based on a frequency domain reflectometry (FDR) method and machine learning tool. The material loss suffered by these rods can lead to their rupture and therefore the guyed tower is about to fall. To avoid this accident, the companies have carries out prevent maintenance on their steel structures using the visual inspection technique on each anchor rod. A nondestructive techniques (NDT) has being developed which uses high frequency signals to detect faults on the anchor rods surface. High frequency pulses are inserted into the anchor rod samples through a vector network analyzer (VNA) for fault detection. The reflection coefficient is analyzed and compared to that obtained in the simulation results. A high frequency connector has been developed in this thesis to interconnect the VNA and the anchor rod. The experimental arrangement also involves, besides the VNA and the high frequency connector, the reference rod. The reference and anchor rods form a transmission line for signal traffic. Samples of anchor rods with and without faults were simulated, fabricated, measured and classified by a machine learning tool to determine the presence or absence of the faults on each of them.

The growing interest in distributed generation systems, especially for renewable energy sources, encourages further research works on DC-DC converters. In photovoltaic applications, it is necessary to increase the voltage to connect the PV module to the grid. Thus, this thesis proposes a family of high step-up DC-DC converters with single switch. The variation of topologies is obtained by modifying the positions of the capacitors and the high gain is achieved through magnetic coupling. The passive clamp circuit reduces the voltage stress across the switch, allowing the use of low R_DSon MOSFET. This leads to low conduction loss of the switch. In addition, the diodes operate with zero-current switching at their turn-off transition, eliminating the reverse recovery losses. The operation principle, steady-state analysis, the main waveforms and the voltage stresses across components are presented. A prototype with rated power of 140W is built to evaluate the performance of the proposed topologies. The highest efficiency obtained is 93.77% at 51.27 W, while at full load, the efficiency is 91.84%.

In recent decades, discrete transforms have received increasing attention with the evolution of digital systems. The signal processing community has focused on developing fast algorithms capable of implementing transforms more efficiently. The extensive research in this area has resulted in algorithms with multiplicative complexity close to the minimum limit. Thus, the approximate transforms emerged as a mathematical tool to avoid multiplication operations. The present dissertation investigates low complexity approximations for a transform with incipient exploration in this sense, the discrete Hartley transform (DHT). Two search methods based on the parameterization of the DHT matrix are proposed and implemented. The first method uses the DHT matrix in its non-factored representation, while the second method makes use of Winograd and Cooley-Tukey factorizations to derive approximations with their fast algorithms. The approximations are obtained by solving an optimization problem that evaluates three objective functions. These functions are metrics of similarity between the approximations and the exact transform. Two of the objective functions are known in the literature, total energy error and orthogonal deviation, and the third metric, called involution error, is proposed in this work. Both search methods derive a total of 45 new approximations of length N = 3, 5, 7, 8, 16, 32. A facial recognition application using MACE filters in Fourier domain was adapted to the Hartley domain. The 18 approximations of length N = 32 obtained were submitted to the face verification system and compared with the exact DHT transform. The approximations presented an average of error rate between 0.32% and 0.50%, some exceeding the exact DHT, which obtained an average of error rate of 0.36%.