Smart environments are increasingly common. By utilizing sensor data from the indoor environment and applying methods like machine learning, they can autonomously control and increase productivity, comfort, and well-being of occupants. The aim of this thesis was to model indoor climate in conference rooms and use K-means clustering to determine quality levels. Together, they enable categorization of conference room quality level during meetings. Theoretically, by alerts to the user, this may enhance occupant productivity, comfort, and well-being. Moreover, the objective was to determine which features and which k would produce the highest quality clusters given chosen evaluation measures. To do this, a quasi-experiment was used. CO2, temperature, and humidity sensors were placed in four conference rooms and were sampled continuously. K-means clustering was then used to generate clusters with 10 days of sensor data. To evaluate which feature combination and which k created optimal clusters, we used Silhouette, Davis Bouldin, and the Elbow method. The resulting model, using three clusters to represent quality levels, enabled categorization of the quality of specific meetings. Additionally, all three methods indicated that a feature combination of CO2 and humidity, with k = 2 or k = 3, was suitable.

Orientador: João Fernando Marar / Banca: Regilene Aparecida Sarzi Ribeiro / Banca: Antonio Tadeu Pellison / Resumo: Objetos conectados e um ambiente sensível capaz de interagir de forma calma e praticamente onipresente entre si e com humanos. Essa é a premissa da computação ubíqua, termo cunhado em 1991 pelo cientista da computação Mark Weiser. Recentemente têm-se ouvido falar de forma cada vez mais frequente de aplicações e dispositivos para internet das coisas, porém existem aplicações complexas demais para uso e outras têm custo proibitivo para implantação em larga escala. O atual projeto tem como objetivo investigar as potencialidades do desenvolvimento de um dispositivo para ser um modelo de neurônio artifical para Internet das Coisas e comunicação M2M utilizando-se do microcontrolador Wi-Fi ESP8266 e da linguagem de programação Micropython. Justificamos para a escolha desse microcontrolador, além do seu baixo custo e simplicidade, o fato de comparativamente ser uma opção mais prática e de baixo consumo de energia, quando comparado com outros dispositivos semelhantes, como Arduino e Raspberry Pi. Para nossa pesquisa utilizaremos além de pesquisa bibliográfica uma proposta prática de utilização do dispositivo com compartilhamento de informações de sensores através da rede wireless. A metodologia utilizada constitui-se na proposta do dispositivo, seu desenvolvimento e testes, bem como a demonstração dos scripts de programação utilizados no sistema. / Abstract: Connected objects and a sensitive environment capable of interacting in a calm and practically omnipresent way with each other and with humans. This is the premise of ubiquitous computing, a term coined in 1991 by computer scientist Mark Weiser. Recently there has been listen in an increasing frequency of applications and Internet devices of things, but there are too many applications that are too complex to use and others have a prohibitive cost for large-scale deployment. The current project aims to investigate the potential of developing a device to be an artificial neuron model for Internet of Things and M2M communication using the Wi-Fi ESP8266 microcontroller and the Micropython programming language. We justify for the choice of this microcontroller, in addition to its low cost and simplicity, the fact that it is comparatively a more practical and low energy option when compared to other similar devices, such as Arduino and Raspberry Pi. For our research we will use besides bibliographical research a practical proposal of use of the device with information sharing of sensors through the wireless network. The methodology used is based on the proposal of the device, its development and testing, as well as the demonstration of programming scripts used in the system. / Mestre

A RESEARCH REPORT SUBMITTED TO THE FACULTY OF COMMERCE, LAW AND MANAGEMENT, UNIVERSITY OF THE WITWATERSRAND, IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF MANAGEMENT SPECIALISING IN ENTREPRENEURSHIP AND NEW VENTURE CREATION Johannesburg, 2016 / Technology entrepreneurship is vital to economic development as it could have positive effects on employment and could rejuvenate industries, especially through disruptive technologies. Studies have shown that South Africa has one of the highest new venture failure rates across the globe and it is, therefore, becoming critical to determine the key factors of successful technology ventures that could contribute to sustaining new technology ventures in South Africa. With the Internet of Things (IoT) fast becoming the most exciting technology trend aimed at transforming everyday business and individual lives, this study set out to investigate the key success criteria for IoT new ventures, as well as key sectors for IoT within South Africa. Linking technology entrepreneurship and the opportunities provided by IoT, this research aims to identify the key success criteria of entrepreneurial ventures within South Africa, as well as analyse the South African IoT ecosystem to determine which sectors provide the greatest opportunity for technology entrepreneurs. This research was conducted through primary research across IoT new technology ventures, as well as enterprises across industries within South Africa. The study found that successful ventures and unsuccessful ventures have different entrepreneur characteristics. A key characteristic for successful IoT ventures is entrepreneur ambitions, while education is the biggest hindrance to unsuccessful ventures. The related car and fleet management industries were found to provide the largest opportunity for IoT entrepreneurship in South Africa. This study provides valuable insight into the IoT market that will aid the sustainability of IoT entrepreneurship. From an academic perspective, it supplements the existing literature on technology and IoT entrepreneurship in South Africa. / MT2016

Devido à sua constante evolução, associada ao surgimento contínuo de novas tecnologias, a computação tem se tornado cada vez mais presente no cotidiano das pessoas, sendo utilizada não só no meio acadêmico e corporativo, mas também no entretenimento e no auxílio às atividades pessoais, entre outros. Nesse contexto, vem ocorrendo um grande avanço em termos de redução de custo e de aumento da variedade de dispositivos do tipo Personal Digital Assistants (PDAs). Esse avanço aumenta a demanda por aplicações que explorem esses equipamentos, mas são poucas as ferramentas que auxiliam no desenvolvimento dessas aplicações. O objetivo da presente pesquisa foi investigar o desenvolvimento de aplicações para dispositivos móveis, especificamente para PDAs, com independência de plataforma. A abordagem adotada foi a extensão da ferramenta MCards, um construtor de aplicações para PDAs, com o propósito de permitir a geração de aplicações para dispositivos móveis com a utilização da tecnologia J2ME (Java 2 Micro Edition). Para tanto, foi necessário, inicialmente, um entendimento do protótipo disponível da ferramenta MCards realizado através da aplicação de um processo de Engenharia Reversa na ferramenta MCards, baseando-se no modelo FUSION-RE/I. Foram executadas, também, manutenções na ferramenta com a finalidade de corrigir imperfeições e problemas de execução e de melhorar suas funcionalidades. Após o processo de Engenharia Reversa e manutenção da ferramenta, foi implementado e incorporado à ferramenta MCards o módulo de geração de aplicações J2ME. O módulo de geração de aplicações J2ME transforma a especificação abstrata, gerada pela manipulação do usuário no editor visual da ferramenta MCards, em código para a tecnologia J2ME com a utilização do perfil CDC / Personal Profile, compilando o código gerado para dar origem aos arquivos da aplicação J2ME que serão utilizados para instalação no dispositivo móvel. / Mobile devices such as Personal Digital Assistants (PDAs) have grown up and number and models, while their cost has been continuously decreasing. Although this scenario indicates that there is a demand for tools that support the development of applications for mobile devices, the literature does not report platform-independent tools. The aim of this research was to investigate the development of applications for mobile devices in a platform-independent way. We have elected to extend an existing prototype tool that supported the development of Palm OS applications, MCards, so that it supported the development of applications using J2ME (Java 2 Micro Edition). We first carried out a reverse engineering process, using FUSION-RE/I, which lead to the understanding of the prototype along with the production of a new, more stable and documented version. We then investigated the process of application generation for the J2ME platform and proposed an extension to MCards that supported both Palm OS and J2ME. In the extended version, the J2ME module transforms an abstract specification, generated with the MCards visual editor, in J2ME code using the CDC/Personal profile. As a result, the extended MCards tool supports the development of applications for the two most common platforms for mobile devices, Palm Os, Pocket PC and Symbian OS.

Thesis advisor: Lillie Richardson Albert / Currently, one of the most popular technology initiatives used in schools to prepare information- and technology-literate students is one-to-one laptop programs. However, limited research studies have investigated factors involved in laptop programs' integration process in schools from various participant perspectives by specifically focusing on ELL students and their needs. Through an ecological lens, this study investigated a one-to-one laptop program integrated into ESL classrooms in an urban middle school, which sustained the program for 6 years. The study included multiple perspectives of various school community members to capture an accurate account of factors necessary for the program's implementation and continuation. This study used a qualitative, single-case research design with exploratory purposes to investigate the multi-level nature of a one-to-one laptop program. Ecology was used as a lens to interpret data and show the relations between living (biotic) and nonliving (abiotic) factors in the program. In-depth data was collected through interviews, classroom observations, field notes, and archives. Collected data were analyzed through constructivist grounded theory using open, axial, and selective coding. The study demonstrated that multiple factors interact with each other and impact the laptop initiative in ESL classrooms. These factors and their interaction were visually represented as a conceptual model. Factors identified in findings were discussed under three main themes: financial, technical and leadership factors. Findings related to financial factors indicated that technical issues increased over the years due to the financial problems, which influenced the instructional use of laptops unfavorably and amplified doubts about the future of the program. Results related to leadership highlighted the importance of having multiple leaderships and allowing the participation of various school members in the decision making process. Results also showed that the federal mandates on achievement influenced the laptop program by changing the vision of the school from teaching with technology to improving instruction and achievement scores on standardized tests. Finally, findings emphasized the importance of including ESL leadership in the laptop program from the very beginning to adjust it to the needs of ESL students. Implications for teachers, administrators, educational researchers, policy makers, and future research are discussed. / Thesis (PhD) — Boston College, 2012. / Submitted to: Boston College. Lynch School of Education. / Discipline: Teacher Education, Special Education, Curriculum and Instruction.

A Computação Ubíqua está revolucionando a interação do ser-humano com os dispositivos computacionais ao disponibilizar tecnologias pouco intrusivas ao cotidiano das pessoas. A Computação Ciente de Contexto, um dos temas de pesquisa em Computação Ubíqua, tem contribuído para a construção de aplicações capazes de customizar-se e adaptar-se às necessidades do usuário sem a intervenção explícita deste. Suportar o desenvolvimento de aplicações Cientes de Contexto é um dos desafios da Computação Ubíqua. Desenvolvido no mesmo grupo de pesquisa que este trabalho está inserido, o Serviço Web Context Kernel, gerencia informações de contexto e explora as especificações e tecnologias da Web como plataforma de intercâmbio para a informação contextual. O trabalho aqui reportado teve como objetivo investigar a utilização de Serviços Web por meio do emprego da infra-estrutura Context Kernel na integração de aplicações em cenários de trabalho colaborativo e de aprendizado eletrônico. Como resultado, foi elaborado um conjunto de lições aprendidas provenientes do estudo e do emprego das especificações para Serviços Web. Outros resultados foram: as especificações de informações de contexto de grupo e de metadados educacionais em dimensões de contexto e exemplos de utilização do Context Kernel para tornar aplicações cientes de contexto. / Ubiquitous computing has became a revolution in terms of the user-computer interaction by providing technologies that seamlessly integrate themselves to people?s everyday life. Context-aware computing, which is an important research theme in ubiquitous computing, has been contributing for the building of applications that are capable both to customize and adapt themselves aiming to support a user without disturb him. A great challenge in ubiquitous computing has been the support to build context-aware aplications. For that reason, the it has been developed the Context Kernel Web Service, which is a service infrastructure that makes use of Web-based specifications and technologies in order to promote the management and the interchange of context information. In this work the usage of Web Services was analyzed by using the Context Kernel to integrate applications in scenarios of cooperative work and e-learning. As a result, a list of lessons learned was ellaborated with respect to the use and the study of Web Services specifications for context-aware computing. Other results include a context information specifications for metadata about group and educational resources as well as examples of how to use the Context Kernel to leverage applications context-aware.

Research that involves human-beings depends on the data collection. As technology solutions become popular in the context of healthcare, researchers highlight the need for monitoring and caring patients in situ. Human Activity Recognition (HAR) is a research field that combines two areas: Ubiquitous Computing and Artificial Intelligence. HAR is daily applied in several service sectors including military, security (surveillance), health and entertainment. A HAR system aims to identify and recognize the activities and actions a user performs, in real time or not. Ambient sensors (e.g. cameras) and wearable devices (e.g. smartwatches) collect information about users and their context (e.g. localization, time, companions). This data is processed by machine learning algorithms that extract information and classify the corresponding activity. Although there are several works in the literature related to HAR systems, most studies focusing on elderly users are limited and do not use, as ground truth, data collected from elder volunteers. Databases and sensors reported in the literature are geared towards a generic audience, which leads to loss in accuracy and robustness when targeted at a specific audience. Considering this gap, this work presents a Human Activity Recognition system and corresponding database focusing on the elderly, raising requirements and guidelines for supportive HAR system and the selection of sensor devices. The system evaluation was carried out checking the accuracy of the activity recognition process, defining the best statistical features and classification algorithms for the Elderly Activity Recognition System (EARS). The results suggest that EARS is a promising supportive technology for the elderly, having an accuracy of 98.37% with KNN (k = 1). / Pesquisas e serviços no campo da saúde se valem da coleta, em tempo real ou não, de dados de ordem física, psicológica, sentimental, comportamental, entre outras, de pacientes ou participantes em experimentos: o objetivo é melhorar tratamentos e procedimentos. As soluções tecnológicas estão se tornando populares no contexto da saúde, pesquisadores da área de saúde destacam a necessidade de monitoramento e cuidado dos pacientes in situ. O campo de pesquisa de Reconhecimento de Atividade Humana (sigla em inglês HAR, Human Activity Recognition) envolve as áreas de computação ubíqua e de inteligência artificial, sendo aplicado nos mais diversos domínios. Com o uso de sensores como câmeras, microfones e acelerômetros, entre outros, um sistema HAR tem por tarefa identificar as atividades que uma pessoa realiza em um determinado momento. As informações coletadas pelos sensores e os dados do usuário são processados por algoritmos de aprendizado de máquina para identificar a atividade humana realizada. Apesar de existirem vários trabalhos na literatura de sistemas HAR, poucos são voltados para o público ancião. Bases de dados e sensores reportados em trabalhos relacionados são voltadas para um público genérico, perdendo precisão e robustez quando se trata de um público específico. Diante dessa lacuna, este trabalho propõe um sistema de Reconhecimento de Atividade Humana voltado para o idoso, levantando requisitos para o sistema HAR assistido e selecionando os dispositivos sensores. Um banco de dados HAR com dados coletados de voluntários mais velhos também é fornecido e disponibilizado. A avaliação do sistema foi realizada verificando a acurácia do processo de reconhecimento da atividade, definindo as melhores características estatísticas e algoritmos de classificação para o sistema de reconhecimento de atividades do idoso. Os resultados sugerem que esse sistema é uma tecnologia de suporte promissora para idosos, tendo uma acurácia de 98.37% com KNN (k = 1).

Classification algorithms represent a rich set of tools, which train a classification model from a given training and test set, to classify previously unseen test instances. Although existing methods have studied classification algorithm performance with respect to feature selection, noise condition, and sample distributions, our existing studies have not addressed an important issue on the classification algorithm performance relating to feature deletion and addition. In this thesis, we carry out sensitive study of classification algorithms by using feature deletion and addition. Three types of classifiers: (1) weak classifiers; (2) generic and strong classifiers; and (3) ensemble classifiers are validated on three types of data (1) feature dimension data, (2) gene expression data and (3) biomedical document data. In the experiments, we continuously add redundant features to the training and test set in order to observe the classification algorithm performance, and also continuously remove features to find the performance of the underlying classifiers. Our studies draw a number of important findings, which will help data mining and machine learning community under the genuine performance of common classification algorithms on real-world data. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

The coastal (terrestrial) and benthic environments along the southeast Florida continental shelf show a unique biophysical succession of marine features from a highly urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a protective marine sanctuary in the southeast (i.e. Florida Keys National Marine Sanctuary). However, the establishment of a standard bio-geomorphological classification scheme for this area of coastal and benthic environments is lacking. The purpose of this study was to test the hypothesis and answer the research question of whether new parameters of integrating geomorphological components with dominant biological covers could be developed and applied across multiple remote sensing platforms for an innovative way to identify, interpret, and classify diverse coastal and benthic environments along the southeast Florida continental shelf. An ordered, manageable hierarchical classification scheme was developed to incorporate the categories of Physiographic Realm, Morphodynamic Zone, Geoform, Landform, Dominant Surface Sediment, and Dominant Biological Cover. Six different remote sensing platforms (i.e. five multi-spectral satellite image sensors and one high-resolution aerial orthoimagery) were acquired, delineated according to the new classification scheme, and compared to determine optimal formats for classifying the study area. Cognitive digital classification at a nominal scale of 1:6000 proved to be more accurate than autoclassification programs and therefore used to differentiate coastal marine environments based on spectral reflectance characteristics, such as color, tone, saturation, pattern, and texture of the seafloor topology. In addition, attribute tables were created in conjugation with interpretations to quantify and compare the spatial relationships between classificatory units. IKONOS-2 satellite imagery was determined to be the optimal platform for applying the hierarchical classification scheme. However, each remote sensing platform had beneficial properties depending on research goals, logistical restrictions, and financial support. This study concluded that a new hierarchical comprehensive classification scheme for identifying coastal marine environments along the southeast Florida continental shelf could be achieved by integrating geomorphological features with biological coverages. This newly developed scheme, which can be applied across multiple remote sensing platforms with GIS software, establishes an innovative classification protocol to be used in future research studies. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

The Smart Campus project envisions a university campus where technology assists faculty, staff, students and visitors to improve and more efficiently accomplish their daily activities. The objective of this project is to develop a smart phone application that assists users in finding a certain location on campus, locating their friends and professors, interacting with any student or professors of the campus, get the count of users at certain locations and remain updated about all the events and campus news. Through this project, an idea of ‘Futuristic Social Network’ in a Campus is modeled and developed on Android platform. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection