The aerodynamic benefits of laminar flow have long made it a sought-after attribute in aircraft design. By laminarizing portions of an aircraft, such as the wing or empennage, significant reductions in drag could be achieved, reducing fuel burn rate and increasing range. In addition to environmental benefits, the economic implications of improved fuel efficiency could be substantial due to the upward trend of fuel prices. This is especially true for the commercial aviation industry, where fuel usage is high and fuel expense as a percent of total operating cost is high. Transition from laminar to turbulent flow can be caused by several different transition mechanisms, but the crossflow instability present in swept-wing boundary layers remains the primary obstacle to overcome. One promising technique that could be used to control the crossflow instability is the use of spanwise-periodic discrete roughness elements (DREs). The Flight Research Laboratory (FRL) at Texas A&M University has already shown that an array of DREs can successfully delay transition beyond its natural location in flight at chord Reynolds numbers of 8.0x10^6. The next step is to apply DRE technology at Reynolds numbers between 20x10^6 and 30x10^6, characteristic of transport aircraft. NASA's Environmentally Responsible Aviation Project has sponsored a transonic laminar-flow wing glove experiment further exploring the capabilities of DRE technology. The experiment will be carried out jointly by FRL, the NASA Langley Research Center, and the NASA Dryden Flight Research Center. Upon completion of a wing glove design, a thorough computational evaluation was necessary to determine if the design can meet the experimental requirements. First, representative CAD models of the testbed aircraft and wing glove were created. Next, a computational grid was generated employing these CAD models. Following this step, full-aircraft CFD flowfield calculations were completed at a variety of flight conditions. Finally, these flowfield data were used to perform boundary-layer stability calculations for the wing glove. Based on the results generated by flowfield and stability calculations, conclusions and recommendations regarding design effectiveness were made, providing guidance for the experiment as it moved beyond the design phase.

Submitted by Ricardo Taoni Xavier null (rtaoni@gmail.com) on 2016-10-31T12:57:11Z No. of bitstreams: 1 DISSERTAÇÃO - RICARDO TAONI XAVIER.pdf: 4280724 bytes, checksum: 935ff61eb52f06aadb80afc3f6386d03 (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-11-04T18:01:56Z (GMT) No. of bitstreams: 1 xavier_rt_me_ilha.pdf: 4135843 bytes, checksum: 8ca33b9b3c8efa0cc08a3c6c6c4a1e4e (MD5) / Made available in DSpace on 2016-11-04T18:01:56Z (GMT). No. of bitstreams: 1 xavier_rt_me_ilha.pdf: 4135843 bytes, checksum: 8ca33b9b3c8efa0cc08a3c6c6c4a1e4e (MD5) Previous issue date: 2016-08-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho, descreve-se a implementação de uma prótese de mão biônica de baixo custo, constituída por um mecanismo com dezesseis graus de liberdade, cinco dedos articulados individualmente, acionados por tendões mecânicos. Sua estrutura foi fabricada em impressora 3D. Para o interfaceamento homem-máquina foi desenvolvido um shield, que realiza a aquisição de sinais eletromiográficos, com dois canais, armazenamento em cartão SD e um sistema eletrônico capaz de gerar um banco de dados com movimentos realizados pela mão humana. Foi utilizada a plataforma Arduino para processamento e programação. O sistema contém um aplicativo com protocolo de acionamento programável e inserção de movimentos, proporcionando independência ao usuário. Foram realizados testes nos quais o sistema implementado executou movimentos, após armazenar vários movimentos funcionais da mão. Isso foi possível devido ao desenvolvimento de um algoritmo capaz de reconhecer, quantificar e armazenar os sinais produzidos pelas contrações dos músculos. A prótese ativa implementada funcionou adequadamente para paciente com deformidade congênita e para paciente amputado. O valor total dos componentes mecânicos e eletrônicos necessários para implementar a prótese ativa e a utilização de hardwares e softwares livres contribuem para que o custo do dispositivo seja reduzido. / This paper describes the implementation a low cost hand prosthesis, a mechanism constituted by sixteen degrees of freedom, five articulated fingers individually, driven by mechanical tendons. Its structure was made in a 3D printer. For the man-machine interfacing, a shield was developed which performs the acquisition of electromyographic signals, with two channels, SD card storage and an electronic system able to generate a database of movements made by the human hand. The Arduino platform was used for processing and programming. The system contains an app with a programmable drive and insertion movements protocol, providing independence to the user. Tests were conducted in which the implemented system performed movements, after storing various functional hand movements. This was possible due to the development of an algorithm capable of recognizing, quantifying and storing the signals produced by muscle contractions. The implemented active prosthesis worked well for patients with congenital deformity and amputee. The total value of the mechanical and electronic components required to implement the active prosthesis and the use of free hardware and software contribute to the low cost of the device.

Orientador: Aparecido Augusto de Carvalho / Resumo: This paper describes the implementation a low cost hand prosthesis, a mechanism constituted by sixteen degrees of freedom, five articulated fingers individually, driven by mechanical tendons. Its structure was made in a 3D printer. For the man-machine interfacing, a shield was developed which performs the acquisition of electromyographic signals, with two channels, SD card storage and an electronic system able to generate a database of movements made by the human hand. The Arduino platform was used for processing and programming. The system contains an app with a programmable drive and insertion movements protocol, providing independence to the user. Tests were conducted in which the implemented system performed movements, after storing various functional hand movements. This was possible due to the development of an algorithm capable of recognizing, quantifying and storing the signals produced by muscle contractions. The implemented active prosthesis worked well for patients with congenital deformity and amputee. The total value of the mechanical and electronic components required to implement the active prosthesis and the use of free hardware and software contribute to the low cost of the device. / Mestre

Healthcare personnel (HCP) frequently wear gloves when they care for patients in Standard Precautions to prevent contact with potentially infectious blood or body fluids. When HCP use gloves appropriately they reduce the risk of cross-contamination and decrease the risk of healthcare-associated infections (HAI). However, if HCP use gloves inappropriately they may inadvertently spread pathogens to patients and the patients’ environment. This study used a descriptive structured observational design to investigate three aspects of HCP glove use in a United States long-term care facility (LTCF). First, the PI examined the degree of inappropriate HCP glove use in a random sample of 76 HCP. Results indicate that the HCP used gloves inappropriately, failing to change gloves 66% of the time when a glove change was indicated. Over 44% of the HCP gloved touch points were defined as contaminated. Second, the PI examined the reliability of a new glove use tool (GUST). Results indicate the GUST is a reliable tool when used by trained observers documenting HCP glove use during toileting and perineal care events in LTCF, with intraclass correlation coefficients (ICC 2,1) over 0.75 for indicators of inappropriate glove use. Third, exploratory analysis indicated significant differences between inappropriate glove use in females and males. Female HCP had significantly more failed glove changes and contaminated touch points than male HCP in this study (p = 0.003). Future research studies should assess US HCP glove use to provide data needed for development of strategies to improve HCP glove use and reduce HAI.

The purpose of the thesis was to create a data glove that can translate ASL by reading the finger- and hand movements. Furthermore, the applicability of conductive fabric as stretch sensors was explored. To read the hand gestures stretch sensors constructed from conductive fabric were attached to each finger of the glove to distinguish how much they were bent. The hand movements were registered using a 3-axis accelerometer which was mounted on the glove. The sensor values were read by an Arduino Nano 33 IoT mounted to the wrist of the glove which processed the readings and translated them into the corresponding sign. The microcontroller would then wirelessly transmit the result to another device through Bluetooth Low Energy. The glove was able to correctly translate all the signs of the ASL alphabet with an average accuracy of 93%. It was found that signs with small differences in hand gestures such as S and T were harder to distinguish between which would result in an accuracy of 70% for these specific signs. / Syftet med uppsatsen var att skapa en datahandske som kan översätta ASL genom att läsa av finger- och handrörelser. Vidare undersöktes om ledande tyg kan användas som sträcksensorer. För att läsa av handgesterna fästes ledande tyg på varje finger på handsken för att urskilja hur mycket de böjdes. Handrörelserna registrerades med en 3-axlig accelerometer som var monterad på handsken. Sensorvärdena lästes av en Arduino Nano 33 IoT monterad på handleden som översatte till de motsvarande tecknen. Mikrokontrollern överförde sedan resultatet trådlöst till en annan enhet via Bluetooth Low Energy. Handsken kunde korrekt översätta alla tecken på ASL-alfabetet med en genomsnittlig exakthet på 93%. Det visade sig att tecken med små skillnader i handgester som S och T var svårare att skilja mellan vilket resulterade i en noggrannhet på 70% för dessa specifika tecken.

This diploma thesis is focused on the experimental determination of thermal resistance of the gloves and their combination in the layering system. The measurement was based on procedure specified in the standard ČSN EN 511 by means of thermal manikin. The thesis begins with the description of heat transfer mechanisms, description of glove materials and measurement methodology. The body of paper consists of the measured data analysis and equation design for determination of the glove layering system thermal resistance. At the end of the thesis, these equations are evaluated and it has been shown, that the thermal resistance of glove layering system cannot be precisely determined by the calculation, and it is necessary to measure the whole three-layer system.

The thesis is focused on the description of the basic principles of the virtual reality, on utilization of data gloves in the virtual reality and it implements the application with usage of the input device P5 glove. The implementation contains among the others various visual techniques of OpenGL and also shows up-to-date work with the popular physics engine Bullet Physics. The theoretical bases of these techniques are analysed in the thesis. So the output of the thesis will serve as a source of information to people interested in the knowledge of the data gloves problems and other similar peripherals, but also to interested people who want to learn more about the realization of the computer graphics and creation of physicist models.

This thesis deals with the named entity recognition (NER) in text. It is realized by machine learning techniques. Recently, techniques for creating word embeddings models have been introduced. These word vectors can encode many useful relationships between words in text data, such as their syntactic or semantic similarity. Modern NER systems use these vector features for improving their quality. However, only few of them investigate in greater detail how much these vectors have impact on recognition and whether they can be optimized for even greater recognition quality. This thesis examines various factors that may affect the quality of word embeddings, and thus the resulting quality of the NER system. A series of experiments have been performed, which examine these factors, such as corpus quality and size, vector dimensions, text preprocessing techniques, and various algorithms (Word2Vec, GloVe and FastText) and their parameters. Their results bring useful findings that can be used within creation of word vectors and thus indirectly increase the resulting quality of NER systems.

Historically author profiling has been used in forensic linguistics. However, it is not until the last decades that the analysis method has worked into computer science and machine learning. In comparison, determining author profiling characteristics in machine learning is nothing new. This paper investigates the possibility to improve upon previous results with modern frameworks using data sets that have seen limited usage. The purpose of this master thesis was to use pre-trained transformers or embeddings together with transfer learning. In addition, to examine if general author profiling characteristics of anonymous users on internet forums or conversations on social media could be determined. The data sets used to investigate the questions above were PAN15 and PANDORA, which contains various properties in text data based on authors paired with ground truth labels such as gender, age, and Big Five/OCEAN. In addition, transfer learning of BERT and GloVe was used as a starting point to decrease the learning time of a new task. PAN15, a Twitter data set, did not contain enough data when training a model and was augmented using PANDORA, a Reddit-based data set. Ultimately, BERT obtained the best performance using a stacked approach, achieving 86 − 91% accuracy for each label on unseen data.

In this thesis, a novel approach to classifying personal data is tested. Previous personal data classification models read the personal data before classifying it. However, this thesis instead investigates an approach to classify personal data by looking at contextual information frequently available in data sets. The thesis compares the well-researched word embedding methods Word2Vec, Global representations of Vectors (GloVe) and Bidirectional Encoder Representations from Transformers (BERT) used in conjunction with the different types of classification methods Bag Of Word representation (BOW), Convolutional Neural Networks (CNN), and Long Short-term Memory (LSTM) when solving a personal data classification task. The comparisons are made by extrinsically evaluating the different embeddings' and models' performance in a personal data classification task on a sizable collection of well-labeled datasets belonging to Spotify. The results suggest that the embedded representations of the contextual data capture enough information to be able to classify personal data both when classifying non-personal data against personal data, and also when classifying different types of personal data from each other. / I denna uppsats undersöks ett nytt tillvägagångssätt att klassificera personlig data. Tidigare dataklassificerings modeller läser data innan den klassificerar den. I denna uppsats undersöks istället ett tillvägagångssätt där den kontextuella informationen används. Uppsatsen jämför flera väletablerade metoder för 'word embedding' så som Word2Vec, Global representations of Vectors (GloVe) och Bidirectional Encoder Representations from Transformers (BERT) i kombination med klassificeringsmodellerna Bag Of Word representation (BOW), Convolutional Neural Networks (CNN) och Long Short-term Memory (LSTM). Modellerna jämförs genom att evaluera deras förmåga att klassificera olika typer av personlig data baserad på namngivning och beskrivning av dataset. Resultaten pekar på att representationerna samt modellerna fångar tillräckligt med information för att kunna klassificera personlig data baserat på den kontextuell information som gavs. Utöver detta antyder resultaten att modellerna även klarar av att urskilja olika typer av personlig data från varandra.