The technological evolution provided by the Internet of Things is in increasing
development and the demand for professionals increases proportionally. Parallel to
this reality, the use of games as a learning tool is a suitable practice especially for the
younger audience, as they present elements of fun and engagement. Serious games
can help players acquire new experiences and complex knowledge, which are
obtained by solving the challenges. In this context, the serious game proposed in this
research, Desafio IoT, aims to provide an overview of some problems and solutions in
embedded software development for smart homes. In addition to a serious purpose,
the game seeks to awaken students' interest in the subject, spreading the idea and
motivating to work in the development area. The implementation of the game was
carried out according to the Learning Mechanics – Game Mechanics (LM-GM)
specification model. In order to investigate the educational impact provided by the
experience of using the game, besides the questionnaires on usage and technical
knowledge, the MEEGA+ questionnaire was used to evaluate the game. The results
allow concluding that the game was able to introduce students to the Internet of Things
area and motivate them to further their knowledge on the subject. The evaluation of
the game by the students presented a positive overall result, as well as approval in
seven of the eight dimensions used in the analysis.

This dissertation seeks to use data science, through unsupervised exploratory analysis, to prove what the literature already knows and/or present new discoveries about the influence of infrastructure on Brazilian basic education. School infrastructure is not limited to the architectural issue of schools, but also to the educational and administrative environment, equipment, educational resources, practices, curricula and the teaching and learning process. Data collection was carried out on open data from the 2019 School Census (Basic Education) and the Basic Education Development Index (Ideb), for the years 2005-2019. The choice of the year 2019 was because it was the last year that schools presented results before the influence of the COVID-19 pandemic. After several data treatments and the choice of attending only the initial segment of fundamental education, two analysis methodologies were applied: Correlogram and Factor Analysis (FA). For clarity in the results, new attributes were created referring to the federative entities that allowed identifying which states and school profiles are better related to the growth and good results of the Ideb. For these correlations, the Sigma of the Gaussian Copula was chosen, which takes into account the categorical and continuous data and also generated a definite positive matrix. The Correlogram generated a square matrix that presented the attribute relationships in a Heatmap Dendrogram. Divided into 4 large groups, each one had specific characteristics and relationships with federal entities. The first group had a strong relationship with basic infrastructure; the second group, with IDEB and the most sophisticated infrastructures; the third group showed few relationships between the attributes; and the last group had strong negative correlations and contained greater precariousness in infrastructure. After verifying the compatibility of the database for the application of the FA, it was estimated that 10 factors would be suitable for this study. Four factors were associated with the attributes of the Ideb, the focus of this work. Three patterns were also observed in the attributes that listed good results in the Ideb with different infrastructures, policies and/or educational proposals: the first group, guided by São Paulo state, presented basic sanitation offered by the public service, quality internet for use in learning and institutions schoolchildren; the second group, headed by Minas Gerais state, indicates an association with flexibility in traditional teaching, with school cycles and non-serial classrooms; the third group was marked by complementary activities and specialized care, represented by the Ceará state . In contradiction to these parameters, schools with the EJA modality, mainly in the northeast, tend to have lower results in the Ideb. The other 6 factors added a lot of relevant information, including those related to the correlations and anti-correlations of the federative entities and specific attributes. As seen, data science has a lot to add to the field of education. Future works are expected to add even more data, such as longitudinal studies on the Ideb and to add other educational indices such as the Ioeb and the socioeconomic level of the population.

This dissertation presents the Differential Transconductance Differences Amplifier (FDDTA) architecture based on CMOS inverters. Designed on a 130 nm CMOS process, it operates in weak inversion when supplied with 0.25 V. Furthermore, FDDTA does not require supplemental external calibration circuitry such as bias current or voltage sources as it relies on the distributed layout technique that intrinsically matches CMOS inverters. For analytical purposes, we performed a detailed investigation that describes all the concepts and the entire functioning of the FDDTA architecture.

Vehicles are becoming more intelligent and connected due to the demand for faster, efficient, and safer transportation. For this transformation, it was necessary to increase the
amount of data transferred between electronic modules in the vehicular network since it is
vital for an intelligent system’s decision-making process. Hundreds of messages travel all
the time in a vehicle, creating opportunities for analysis and development of new functions
to assist the driver’s decision. Given this scenario, the dissertation presents the results of
research to characterize driving styles of drivers using available information in vehicular
communication network. This master thesis focuses on the process of information extraction from a vehicular network, analysis of the extracted features, and driver classification based on the extracted data. The study aims to identify aggressive driving behavior using real-world data collected from five different trucks running for a period of three months. The driver scoring method used in this study dynamically identifies aggressive driving behavior during predefined time windows by calculating jerk derived from the acquired data. In addition, the K-Means clustering technique was explored to group different behaviors into data clusters. Chapter 2 provides a comprehensive overview of the theoretical framework necessary for the successful development of this thesis. Chapter 3 details the process of data extraction from real and uncontrolled environments, including the steps taken to extract and refine the data. Chapter 4 focuses on the study of features extracted from the preprocessed data, and Chapter 5 presents two methods for identifying or grouping the data into clusters. The results obtained from this study have advanced the state-of-the-art of driver behavior classification and have proven to be satisfactory. The thesis addresses the gap in the literature by using data from real and uncontrolled environments, which required preprocessing before analysis. Furthermore, the study represents one of the pioneering studies conducted on commercial vehicles in an uncontrolled environment. In conclusion, this thesis provides insights into the development of driver behavior classification models using real-world data. Future research can build upon the techniques presented in this study and further refine the classification models. The thesis also addresses the threats to validity that were mitigated and provides recommendations for future research.

One of the existing goals in the 2030 Agenda for Sustainable Development is to increase inclusive and sustainable urbanization. Achieving this goal is already considered a challenge, since in Brazil many cities have already gone through, and still go through, a process of expansion and urbanization. In this context, the concept of urban planning emerges, understood as one of the ways to systematize this process, since it allows a better allocation of financial and human resources, in addition to defining actions and objectives in favor of solving collective problems. For this, urban policy instruments are defined, closely related to urban planning, since urban policy instruments are the tools and mechanisms used to implement planning and achieve the goals established for the city. The objective of the research presented in this master's thesis is to investigate solutions that support decision-making in the context of urban planning. For this, data collected in projects for the elaboration and updating of urban policy instruments were analyzed so that these data could be used in the elaboration of a solution. The methodology used was the Design Science Research Methodology (DSRM), the artifact proposed and developed was a decision support system in the form of a visualization panel of information about the spatial composition of a municipality in the interior of Minas Gerais . For this, concepts of geographic data analysis and information visualization were used. The panel was evaluated by a group of potential users and the hypothesis that the use of data generated by urban policy instruments can help municipal urban planning was corroborated by the responses obtained. With the study, it was possible to highlight the importance of the data considered, the potential of the proposed artifact, in addition to identifying opportunities for future work.

The COVID-19 pandemic has made it clear the high demand for computational systems
that expedite the discovery of new drugs. In this regard, understanding the dynamic
behavior of biomolecular complexes is crucial. Techniques involving molecular
dynamics simulations of these complexes have increasingly been used to accelerate the
identification of better drug candidates. However, the preparation of such simulations is
highly complex, and their numerous details are not always adequately emphasized,
compromising their reproducibility and reusability. To address this, the ProtCool tool was
proposed—a protocol generator focused on integrating docking and molecular dynamics
of protein-ligand complexes. In its initial version, this tool was restricted to the user's
local environment. This work presents version 2.0 of ProtCool, developed under a clientserver model with a web interface. The aim is to fill the gaps left by the previous version,
enhancing the software in three fundamental aspects: making it multi-platform, enabling
access to multiple users, and making the tool more intuitive. The development of a userfriendly interface allows this new version to expand its scope of use to inexperienced or
novice researchers in computational chemistry. ProtCool 2.0 does not execute the
dynamics or perform result analyses; it is designed to be an expert in preparation based
on workflows, following the programmed workflow and generating all the necessary
configuration files for reliable execution of molecular dynamics on the user's
computational setup. With the entire process being properly recorded, this allows for
greater reproducibility and reusability of the preparations. Many of its functionalities are
based on the adaptation of well-known tools from the literature in the field of molecular
dynamics simulations. ProtCool 2.0 was developed using best practices and software
engineering processes. Its client-server architecture implemented under the web standard
enables it to be cross-platform and multi-user, providing benefits in availability and
performance. It features a minimalist graphical interface with interactive resources that
ensure user safety in correctly filling out their study parameters, thus preventing errors.
To demonstrate its use, ProtCool 2.0 underwent a case study on the simulation of the
interaction between acetylcholinesterase and galantamine, used in the treatment of
Alzheimer's disease, which allowed for validation through replication of a simulation
certified by peers in an international publication. The preparation of the simulation
successfully enabled the execution of reliable molecular dynamics, reproducing the
expected results. It is expected that this tool will not only bring greater speed,
reproducibility, and reusability to molecular dynamics preparations but also contribute to
smoothing the learning curve for these simulations in computational chemistry.

Over the past decade, the Internet has changed the way people work, shop and socialize. Those changes resulted in the increase of User Generated Content (UGC) such as: ratings, reviews, wikis, and videos. UCG contains relevant information for decision-making, especially with regard to the acquisition of goods and services. However, the large volume and dispersion of this content makes it difficult to obtain relevant information. Text summarization appears as a way to make this content more accessible to people.
A summary A can be considered better than another B when A is shorter than B while maintaining the same content relevance, or when A, despite being longer, presents more relevant content. Analyzing the literature, we observed that it is possible to produce better quality summaries than those produced by algorithms that correspond to the state of the art in text summarization. We present a multilingual automatic text summarizer that combines and extends the algorithms Latent Dirichlet Allocation (LDA) and TextRank. Our approach, when compared to the state of the art, generates better text summaries in terms of size and content relevance.

A multidisciplinary study conducted in Minas Gerais investigated the temporal dynamics
of street robberies, analyzing both pre-pandemic and pandemic periods. Utilizing data
from the Military Police, time series data were examined at various scales, including
hourly, daily, 10-day intervals, and monthly, employing advanced statistical methods such
as spectral frequency analysis, autocorrelations, and decomposition techniques.

This work highlights a comprehensive investigation into the application of Artificial Intelligence (A.I.) in human resources management, with a specific focus on identifying employee dissatisfaction through machine learning approaches. The research included a review of scientific articles discussing both the implementation of A.I. in the context of human resources and the use of machine learning techniques to detect cases of turnover/attrition, along with the relationship between dissatisfaction and turnover/attrition cases.
To assess these approaches, four validated public databases were selected. Three of them contained fictional employee data, and one contained real employee turnover data. Each database underwent a process of textual field factorization, followed by analyses to highlight the data distributions in each set.
In conducting the research, different machine learning approaches were applied to each of the databases, aiming to verify the feasibility of identifying dissatisfaction through A.I. The techniques used included anomaly or novelty detection, classifiers, and optimized sets of classifiers. The results were quantified, revealing promising scores, with performances exceeding 90%. These results emphasize the overall effectiveness of machine learning in identifying employee dissatisfaction, demonstrating its potential for practical applications in the human resources environment.

Public consultation is a direct democracy device that aims to allow the population to participate in decision-making in various areas of public policy. In Brazil, the Fiscal Responsibility Law establishes that a public consultation must precede the preparation of the public budget, which aims to regulate the state's financial revenue and expenditure activities for one year. This example of public consultation is called Participatory Budgeting. However, population engagement is still a problem for several reasons. Therefore, it is important to investigate strategies that increase citizen participation. In some scenarios, Nudges theory can be used. Nudges are small, low-investment modifications made to a person's environment to change their behavior. The application of Nudge in this research aims to encourage citizens to participate in a public consultation context. To enable analysis, it was part of the scope of this research to propose and develop a tool. The application was analyzed in a real scenario which allowed us to conclude that the results with the application of Nudge were adequate.

Periodically the electric power transmission lines need to be inspected to avoid future
problems or even to solve problems that have been noticed. In order to carry out these
inspections, companies have been trying to make it as safe as possible so that there is not
a very high expense and do not put any person at risk, for this they are increasingly using
drones to inspect, however these drones need to have people nearby operating navigation
and with that it often ends up not being as efficient as it ends up passing some problem.
To solve the problem of detecting a possible problem on the line I come to present a
solution using three drones that will make an autonomous flight over the line. The first
drone will initially travel to the first tower and with a camera, using a convolutional
neural network it will detect the tower and any possible problem that is found in it and
record the location of the problem tower for someone to do maintenance on it, the the
second drone will be responsible for checking the wires that conduct the electrical energy
and the third drone will be responsible for detecting if there is something under the line
that may be at risk or have come to cause future problems for the line.
The solution was implemented in the airsim simulator, where an environment already
available by the software for autonomous navigation tests with a high voltage line was
used. The navigation of the drones was made from the positioning of the towers where
the location of the latitude and longitude of each one is already known.

Software modeling is considered one of the most important topics in software engineering education. Currently, the Unified Modeling Language (UML) is the most widespread and used software modeling language in the software engineering industry. Although the UML is constantly being improved and studied, many studies show that there is difficulty in teaching and learning the subject, due to the complexity of its concepts and the students' cognitive difficulties with abstraction. Also, students face difficulties in understanding the semantics and syntax of models, as well as structuring the information in these models. In addition, there are difficulties for teachers in finding different pedagogical strategies, in order to teach modeling. In this sense, some researches thus search for new tools, techniques or methodologies that help teachers and motivate students regarding the study of UML. This work proposed the development of a web platform to support the studying of software modeling with the UML, using gamification resources. The platform proposed allowed students to complement their UML knowledge in an environment with game elements. Aiming to investigate the impact of using the developed gamified platform, a case study was carried out to evaluate the user experience and satisfaction from the student perspective. From the results, it can be concluded that the platform obtained great acceptance and satisfaction of use. Most of the students participating in the research were satisfied with the usability of the platform, reporting a feeling of contribution of the tool in the studying of the content, in addition to pointing out the satisfaction of using gamification as a pedagogical strategy. As a result, the platform was effective in terms of engaging and motivating students, being a complement to the traditional teaching method.

Autism Spectrum Disorder (ASD) is an age- and sex-related lifelong neurodevelopmental disorder characterized primarily by social impairments. Current ASD prevalence indicates that 1/59 children are diagnosed inside the spectrum. The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) classifies ASD according to the disorder severity. ADOS-2 classifies as 'autism' cases that manifest more severe symptoms and as 'ASD non-autism' cases that exhibit milder symptoms. Many papers aimed to create algorithms to diagnose ASD through Machine Learning (ML) and functional Magnetic Resonance Images (fMRI). Such approaches evaluate the oxygen flow in the brain to classify the subjects as ASD or typical development. However, most of these works, do not provided information regarding the disorder severity. This paper aims to use ML and fMRI to classify the disorder severity, aim to find brain regions potentially related to the disorder severity. We used fMRI data of 202 subjects and their ADOS-2 scores available at the ABIDE consortium to determine the correct ASD sub-class for each one. Our results corroborate the initial hypothesis of functional differences within ASD, with some brain regions where the functional difference was enough to create classification accuracy of 74%. This paper has limitations regarding the total number of samples. However, it shows a promising approach to ASD diagnosis.

High power power transmission lines are of great importance for the functioning of all sectors of society. To guarantee the reliability and availability of the energy supply, regular and occasional inspections are carried out, always manually. These inspections seek to identify anomalies in the transmission network and signs of danger to structures. Unmanned aerial vehicles, popularly known as drones, have been increasingly employed in repetitive and tedious activities, due to their low cost and increased safety, and can be applied to tracking these structures. However, the variety of environments and towers, error in location measurements, and high cost are difficult obstacles to overcome for accurate and quality inspection. The main objective of this work is to study, develop and compare real-time image processing techniques to determine movements to be performed by the drone in trajectory correction, and thus perform a tracking of transmission lines with high precision and low cost. Several solutions are found in the literature, and are located and systematically reviewed in this study. Regarding the analysis of the hardware line, controller boards and frameworks were compared, as well as ground station and simulator software, with the main focus on image processing using edge detector, deep learning and reinforcement learning. Furthermore, in relation to the training of networks, a dataset was made for identifying lines, and another for actions to be performed. Also, in order to evaluate the solution's performance, a realistic virtual scenario based on a real route was produced. In short, a system based on a prototype equipped with two quadricopter drones equipped with only a camera and a computer, working in conjunction with a cooperative algorithm and deep learning image processing, resulted in a brief tracking in a power transmission line, simulating an inspection in a real environment.

The teaching and learning of programming, in general, has proven to be a challenge for students of computer and related courses, since they present challenges and require complex skills, such as logical-mathematical reasoning, for their good development. Furthermore, the traditional teaching model is not able to motivate students and arouse their interest in the topic. There is, therefore, a need for the actors involved in education to review their way of teaching, integrating information and communication technologies in education in the same way as they are present in our daily lives. In this sense, the use of an Open Educational Resource (OER) not only has the potential to contribute to the modernization of the current teaching model, but also to collaborate on improving the quality of education. The tool proposed herein, the REA-LP, aims to facilitate studying and retention of content related to the discipline of programming logic at the technical level, by presenting its content through various types of media, such as audio, video, text, and static and dynamic images. Therefore, the tool allows students to actively participate in the construction of their knowledge, favoring engagement and motivation, in addition to enabling the review of content considered essential for the proper development of the discipline. To assess the perception of use by students and its impact on the study of algorithms and programming logic, as well as to evaluate the resource pedagogically, an empirical study was carried out with 39 students of an informatics technical course integrated with secondary school. In the research, some questionnaires were applied, as well as focus groups interviews and a performance test were carried out. The results allow concluding that the tool was very well accepted, being effective in its function of facilitating and assisting students in their learning, motivation, and interest in classes, mainly due to the way in which the content is presented at REA-LP, with emphasis on animated media and review modules, exceeding their expectations.

Big data technology is increasingly present in industry and academic research by providing value to massive data analysis. Improvement of this technology is necessary due to the exponential growth of information provided by various sources present in our daily lives, such as environment sensors, cameras, smartphones and social networks. A big challenge for big data applications is balancing their performance against their operating costs. Thus, the Big Data application provider needs to continuously monitor their system to identify improvement points within their architecture. In this context, this work develops a refernce method for performance evaluation for Big Data architectures, called by Optimization Model for Big Data Architectures (OMBDA) aimming to improve the performance and consequently raising quality of service provided. Its main contribution is directed to small companies and startups that have limited financial resources, so investing in ready-made solutions in the market is impossible. The proposed model considers the relationship of processes within a data processing flow to find possible bottlenecks and optimization points. To this end, OMBDA collects system logs to compose functional metrics (e.g., processing time) and non-functional metrics (e.g., CPU and memory utilization, and other cloud computing infrastructure resources). These metrics are stored in an external data analysis tool that investigates the correlation of performance between processes. In this work, the model is applied to the architecture of a Big Data application, which provides solutions in fleet logistics. Through the application of OMBDA it was possible to identify performance bottlenecks, enabling the architecture reconfiguration to increase the quality of service at the lowest possible cost.

This work investigates the implementation of an autonomous control system for dro-
nes, this system is capable of crossing windows on flights through closed places, through

image processing and the use of convolutional neural networks. The strategy used is the
detection of objects, through its location in the captured image it is possible to carry out

a programmable route for the drone. In this study, this location of the object was esta-
blished by bounding boxes, which define the quadrilateral around the found object. The

system was based on the use of an open source autopilot, Pixhawk, which has a control

and simulation environment capable of doing the job. Two detection techniques were stu-
died, the first based on image processing filters, which captured polygons that represent

a passage inside a window. The other approach was studied for a more real environment,
implemented with the use of convolutional neural networks for object detection, with this

type of network it is possible to detect a large number of windows. The system also in-
volves the implementation of a control method, which is able to apply the data captured

by the detectors and perform the calculation of the appropriate movement speed. The
study brings evaluations of the system and demonstrations of the tests performed for its
validation.

The retroactivity in programming is the study of a modification in a timeline for a data structure and the effects that this modification exerts throughout its existence. In general, the analysis and implementation tend to be more costly computationally, because a modification on these data structure in the past can generate a cascade effect through all the data structure timeline. The concept of retroactivity generates tools and structures that optimize the solutions facing these temporal problems. This type of data structure can be used in, for example, shortest path algorithms, security applications, and geometric problems. In
this thesis, we have the theoretical subsidies about these data structures, a detailed material about the implementation of this structures, using retroactivity, and the implementation of some problems that retroactivity can be used, for example, the fully dynamic minimum spanning tree problem. For each data structure, we executed practical tests about this data retroactive data structures and a comparison between these solutions and other approaches. The tests showed that the retroactive implementations proposed by Demaine et. al (2007) obtained the best results from a temporal point of view. It was proposed two algorithms which used the retroactivity concepts inside its development: the fully retroactive minimum spanning tree and the single source dynamic shortest path problem in dynamic graphs. Let m be data structure’s timeline, V (G) and A(G) the sets of vertices and edges from graph G. We reached an amortized time complexity O( √m · lg |V (G)|) per query/update operation in the fully retroactive minimum spanning tree algorithm. The algorithm to solve the single source dynamic shortest path problem in dynamic graphs proposed by Sunita et. al [52] obtained a time complexity O(|A(G)| · lg |V (G)|) per modification using a non-oblivious retroactive priority queue.

Survey over the years have become a practical and common way of collecting data using software for the most diverse purposes. Participation in Survey can be considered a phenomenon in which collaborative systems can be applied, since these systems are designed to allow people to communicate and work together in a shared space. In this approach, the survey can be considered as an instrument that operationalizes the cooperation between a research requester and the respondent. However, participation in Survey, regardless of the modality, is greatly influenced by the lack of engagement of the participants. Gamification, which is defined as the use of game elements and characteristics in non-game contexts, has been considered an approach to increase engagement and encourage participation in Survey. Thus, the objective of this dissertation was to investigate how gamification can be used as a motivational factor to stimulate engagement in Survey. The methodology of this work was defined based on the epistemological paradigm Design Science Research (DSR), which was used to carry out two research and two artifacts were developed, the Collaborative Engagement Model and the Opina Aí application. The artifacts were evaluated in two experiments in which two versions of the application were compared, gamified and non-gamified versions. Although the results regarding the impact of gamification on engagement have not been conclusive, this work can expand knowledge and support future studies in contexts where participation can be exercised collaboratively and provide guidance on the use of gamification in Survey.

With industry 4.0, data-based approaches are in vogue. However, extracting the essential
features is not an easy task and greatly influences the final result. There is also a need
for specialized system knowledge to monitor the environment and diagnose faults. In this
context, the diagnosis of faults is significant, for example, in a vehicle fleet monitoring
system, since it is possible to diagnose faults even before the customer is aware of the
fault, in addition to minimizing the maintenance costs of the modules. In this work,
several models using Machine Learning (ML) techniques were applied and analyzed during
the fault diagnosis process in vehicle fleet tracking modules. This research proposes two
approaches, with knowledge and without knowledge, to explore the dataset using ML
techniques to generate classifiers that can assist in the fault diagnosis process in vehicle
fleet tracking modules. The approach with knowledge performs the feature extraction
manually, using the ML techniques: Random Forest, Naive Bayes, SVM and MLP; and
the approach without knowledge performs an automatic feature extraction, through a
Convolutional Neural Network (CNN). The results showed that the proposed approaches
are promising. The best models with manual feature extraction obtained a precision of
99,76% and 99,68% for detection and detection and identification of faults, respectively,
in the provided dataset. The best models performing an automatic feature extraction
obtained respectively 88,43% and 54,98% for detection and detection and identification of
failures. These models can serve as prototypes to diagnose faults remotely and confirm that
traditional ML techniques with manual extraction of features are still effective resources
for fault diagnosis.

This work presents an extension of the Earned Value Management (EVM) technique.
The proposed technique integrates quality data, based on quality requirements, to update
traditional EVM indexes.
The main objective of this proposal is to improve the predictability of the Cost Performance
Index (CPI) and the Schedule Performance Index (SPI) and introduce quality
measures to the EVM technique.
The proposed technique was evaluated in accordance with an evidence-based methodology.
The studies conducted showed a better accuracy in the proposed technique of extension
of the EVM technique in relation to the traditional EVM technique

Blockchain is the technology that allows the transaction of money between parties in
a peer-to-peer manner without the need of a trusted intermediary such as banks. This
technology is known for its attractive characteristics: (i) the data integrity, and (ii) security.
To take advantage of such characteristics, some blockchain networks are not focused
on their cryptocurrency. One of those networks is the Ethereum network, which is a
platform for smart contracts. Smart contracts are applications that represent an agreement
between parties in a decentralized manner. These algorithms are deployed on the system
and can be globally accessed. They are executed without the possibility of censorship,
fraud, or external third party intervention. One of their possible applications is the smart
property concept that is transacting property on the blockchain. This can be applied on
the property rental context and the raising use of services such as AirBnB due to its
financial attractiveness to its users, there is a need to develop a system capable of solving
the security issues while providing comfort. This work will presents the development of a
project that studies the viability of applying the concept of smart property. A reservation
platform was developed where the user makes one reservation and, when approved, a
smart contract is deployed to the Ethereum platform. To access the property, the user
uses the application developed for Android which transfer the credentials using a data
through sound protocol. The lock verifies these credentials accessing the smart contract
and activates the lock’s circuit allowing access to the property. To examine the viability of
this project, tests were performed with supposed users of this system who also answered a
survey. After the analysis of this survey, the project proved to be viable, more attractive
than the traditional standard, functional, and provided comfort.

The Brazilian electricity transmission network requires regular preventive inspection and maintenance to ensure its supply to consumers. Transmission companies carry out regular inspections to identify anomalies in the transmission network, such as cable defects, insulator cracks and structures close to the network, such as trees, among others. These inspections can be performed by expert personnel using binoculars or by manned helicopters containing a set of sensors, such as infrared cameras to detect hot spots, among other sensors. Interest in using unmanned aerial vehicles (UAVs) to conduct transmission line inspections has grown in recent years due to the lower cost and increased safety compared to manned helicopter inspections. With this technology, the UAV can be controlled via radio control by a pilot or semi automatic, with a pre-programmed mission in the aircraft’s onboard pilot. One of the major difficulties in performing autonomous flights is the dependence on GPS (Global Positioning System) to obtain the UAV position. Errors in GPS position measurements can affect mission progress. This project aims to develop a real-time image processing technique to identify and track transmission lines and thus determine their position relative to the UAV. Without knowing exactly the position of the UAV and the transmission lines, but knowing the relative position between them, it can be possible to correct the UAV trajectory through commands sent to the aircraft autopilot. This project was developed under the ROS (Robot Operating System) framework and the image processing was performed using the OpenCV library. To evaluate the performance of the solution, a virtual scenario was created in the Gazebo robot simulator, which made it possible to process the images generated by a multirotor during an inspection mission.