Compressão de Imagens Usando a Função de Peano e a Transformada Wavelet 1D

In this work, spoke about the importance of image compression for the industry, it is known that processing and image storage is always a challenge in petrobrás to optimize the storage time and store a maximum number of images and data. We present an interactive system for processing and storing images in the wavelet domain and an interface for digital image processing. The proposal is based on the Peano function and wavelet transform in 1D. The storage system aims to optimize the computational space, both for storage and for transmission of images. Being necessary to the application of the Peano function to linearize the images and the 1D wavelet transform to decompose it. These applications allow you to extract relevant information for the storage of an image with a lower computational cost and with a very small margin of error when comparing the images, original and processed, ie, there is little loss of quality when applying the processing system presented . The results obtained from the information extracted from the images are displayed in a graphical interface. It is through the graphical user interface that the user uses the files to view and analyze the results of the programs directly on the computer screen without the worry of dealing with the source code. The graphical user interface, programs for image processing via Peano Function and Wavelet Transform 1D, were developed in Java language, allowing a direct exchange of information between them and the user

INTEGRATION OF INSTRUMENTATION AND PROCESSING SOFTWARE OF A LASER SPECKLE CONTRAST IMAGING SYSTEM

Laser speckle contrast imaging (LSCI) has the potential to be a powerful tool in medicine, but more research in the field is required so it can be used properly. To help in the progression of Michigan Tech's research in the field, a graphical user interface (GUI) was designed in Matlab to control the instrumentation of the experiments as well as process the raw speckle images into contrast images while they are being acquired. The design of the system was successful and is currently being used by Michigan Tech's Biomedical Engineering department. This thesis describes the development of the LSCI GUI as well as offering a full introduction into the history, theory and applications of LSCI.

DEVELOPMENT OF ION EXCHANGE MODELS FOR WATER TREATMENT AND APPLICATION TO THE INTERNATIONAL SPACE STATION WATER PROCESSOR

The International Space Station (ISS) requires a substantial amount of potable water for use by the crew. The economic and logistic limitations of transporting the vast amount of water required onboard the ISS necessitate onboard recovery and reuse of the aqueous waste streams. Various treatment technologies are employed within the ISS water processor to render the waste water potable, including filtration, ion exchange, adsorption, and catalytic wet oxidation. The ion exchange resins and adsorption media are combined in multifiltration beds for removal of ionic and organic compounds. A mathematical model (MFBMODEL™) designed to predict the performance of a multifiltration (MF) bed was developed. MFBMODEL consists of ion exchange models for describing the behavior of the different resin types in a MF bed (e.g., mixed bed, strong acid cation, strong base anion, and weak base anion exchange resins) and an adsorption model capable of predicting the performance of the adsorbents in a MF bed. Multicomponent ion exchange ii equilibrium models that incorporate the water formation reaction, electroneutrality condition, and degree of ionization of weak acids and bases for mixed bed, strong acid cation, strong base anion, and weak base anion exchange resins were developed and verified. The equilibrium models developed use a tanks-inseries approach that allows for consideration of variable influent concentrations. The adsorption modeling approach was developed in related studies and application within the MFBMODEL framework was demonstrated in the Appendix to this study. MFBMODEL consists of a graphical user interface programmed in Visual Basic and Fortran computational routines. This dissertation shows MF bed modeling results in which the model is verified for a surrogate of the ISS waste shower and handwash stream. In addition, a multicomponent ion exchange model that incorporates mass transfer effects was developed, which is capable of describing the performance of strong acid cation (SAC) and strong base anion (SBA) exchange resins, but not including reaction effects. This dissertation presents results showing the mass transfer model's capability to predict the performance of binary and multicomponent column data for SAC and SBA exchange resins. The ion exchange equilibrium and mass transfer models developed in this study are also applicable to terrestrial water treatment systems. They could be applied for removal of cations and anions from groundwater (e.g., hardness, nitrate, perchlorate) and from industrial process waters (e.g. boiler water, ultrapure water in the semiconductor industry).

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

A next application prediction service using the BaranC framework

Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Predicting the concentration of residual methanol in industrial formalin using machine learning

In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to provide the best results and was used to create the predictive model. The model was compiled to a stand-alone application with a graphical user interface using the MATLAB CompilerTM.

1H NMR Relaxation Characterization Of Hydrates

To distinguish the components of NMR signals from hydrated materials and to monitor their evolution after the addition of water to the powders, during the first two days of hydration. To implement the 3 Tau Model in a MATLAB script, called 3TM, provided with a Graphical User Interface (GUI), to easily use the 3 Tau Model with NMRD profiles. The 3 Tau Model, developed a few years ago is used for interpreting the dispersion (NMRD profiles, dependence on the Larmor frequency) of the longitudinal relaxation times, for liquids confined in porous media. This model describes the molecular dynamics of confined molecules by introducing three characteristic correlation times and additional outputs.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.

An Image Recognition Software for Identification of Vehicle Homologation Tags

In recent years, we have witnessed great changes in the industrial environment as a result of the innovations introduced by Industry 4.0, especially in the integration of Internet of Things, Automation and Robotics in the manufacturing field. The project presented in this thesis lies within this innovation context and describes the implementation of an Image Recognition application focused on the automotive field. The project aims at helping the supply chain operator to perform an effective and efficient check of the homologation tags present on vehicles. The user contribution consists in taking a picture of the tag and the application will automatically, exploiting Amazon Web Services, return the result of the control about the correctness of the tag, the correct positioning within the vehicle and the presence of faults or defects on the tag. To implement this application we ombined two IoT platforms widely used in industrial field: Amazon Web Services(AWS) and ThingWorx. AWS exploits Convolutional Neural Networks to perform Text Detection and Image Recognition, while PTC ThingWorx manages the user interface and the data manipulation.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Sviluppo di un modello previsionale del comportamento magnetico di leghe Fe-Si

Il presente lavoro di tesi ha lo scopo di implementare un modello previsionale del comportamento magnetico di leghe Fe-Si in regime statico e dinamico, mediante l’identificazione e l’ottimizzazione dei parametri caratteristici del modello di Jiles-Atherton. Tale fine è stato perseguito attraverso l’uso del software di calcolo Matlab-Simulink. Il modello, validato mediante i dati reperibili in letteratura, permette di simulare la curva di prima magnetizzazione e il ciclo di isteresi per materiali ferromagnetici soft mediante la conoscenza di un set limitato di dati sperimentali ricavabili dalle prove magnetiche. Il modello è stato impiegato per eseguire la simulazione anche su campioni prodotti industrialmente in acciaio al silicio a grano non orientato forniti allo stato fully-processed M470-50A preventivamente sottoposti a caratterizzazione microstrutturale, mediante microscopia ottica, elettronica e analisi EBSD, e meccanica, attraverso le prove di trazione. I risultati ottenuti dalla simulazione presentano ottimale accuratezza, in particolar modo nel caso statico sia in termini di estrazione dei parametri sia di definizione del ciclo; risulta ancora da migliorare ulteriormente il grafico in frequenza. Al fine di rendere fruibile il modello realizzato è stata progettata una Graphical User Interface. Nell’ottica di una mobilità green in accordo con gli obiettivi globali, l’implementazione del presente modello pone quindi le basi per uno studio futuro del comportamento dei materiali magnetici per la realizzazione di motori elettrici sempre più performanti in funzione dei parametri di produzione e delle condizioni di utilizzo, aspetti che incidono notevolmente sulle proprietà di tale materiale.

User-centered design of the interface prototype of a business intelligence mobile application

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

Fast robot voice interface through optimum-path forest

Voice-based user interfaces have been actively pursued aiming to help individuals with motor impairments, providing natural interfaces to communicate with machines. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for voice-based robot interface, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster. Experiments were conducted against Support Vector Machines, Neural Networks and a Bayesian classifier to show the OPF robustness. The proposed architecture provides high accuracy rates allied with low computational times. © 2012 IEEE.

A software development process model for gesture-based interface

Gesture-based applications have particularities, since users interact in a natural way, much as they interact in the non-digital world. Hence, new requirements are needed on the software design process. This paper shows a software development process model for these applications, including requirement specification, design, implementation, and testing procedures. The steps and activities of the proposed model were tested through a game case study, which is a puzzle game. The puzzle is completed when all pieces of a painting are correctly positioned by the drag and drop action of users hand gesture. It also shows the results obtained of applying a heuristic evaluation on this game. © 2012 IEEE.

Online Shopping Websites: An Evaluation of User Experience and Interface Ergonomic Criteria from the Perspective of Older Users

This paper presents a user experience evaluation of two online shopping websites from the perspective of older users (those aged 50 and older). Two online shopping websites were evaluated using methodological procedures established in prior research [1]. The methodology consists of four steps: (1) heuristic interface evaluation using an ergonomic criteria checklist, (2) online identification and experience questionnaire, (3) evaluation of user experience and interface interaction, and (4) satisfaction questionnaire. Results of the study revealed the analyzed websites are not suitable for older users, who find it difficult to interact with these interfaces.