This master's thesis deals with design of universal data logging device. The work proposes the design of device architecture and describes individual systems and components, that are necessary for data logging. Logger is designed as a mobile device, with ARM microcontroller at its core. Thesis specifies requirements for device in terms of performance and versatility. The device uses modular construction, as it comprises multiple PCBs. The thesis describes their design and also design of the mechanical construction of data logger. Designed device has been realized and assembled. Software design for microcontroller is also part of the thesis. In the last part, measurements to determine achieved parameters of the device are conducted.

The top of this thesis is design and implementation of software product for PDA's. It allows navigation with the support of internet maps, communication between participants and recording of expert information. The target platform is the Windows Mobile 6 with .NET Framework.

(in English): Thesis deals with the phenomenon of m-commerce. History section briefly describes the evolution and significant moments for further development of this segment and its subsequent adaptation. It also discusses the types of transactions that are carried out through mobile devices and also about technologies that allow for a gradual transformation of these devices into multifunctional objects. The fourth chapter deals with the evaluation of practical research, which was carried out by me. The research was focused on the typology of purchases that are realized by Czech users through their mobile devices (smartphones and tablets) in relation to mobile smartphone platform, which they own.

Using a modified still-face procedure the present study explores 3-6-month-old infants' behavioral and physiological responses to mothers' screen distractions during mother-infant interactions. In the modified phone still-face procedure the neutral face of the traditional still face procedure was replaced with mothers' texting on their mobile phones. Infants' cortisol stress responses to mothers' device use were assessed through the collection of 3 infant saliva samples. Infants' behavioral responses including facial expressions, vocalizations, gaze and self-comforting behaviors were also explored. All mother-infant interactions were videoed recorded and coded for analysis. Thirty-four mother-infant dyads participated, average ages for mothers was 29 years and 4.4 months for infants. As predicted, infants demonstrated the changes in affect associated with the still-face effect, with significant differences in positive and negative affect during the play phases and the phone still face phase. As a whole, infants did not respond with increased cortisol responses, however, when individual differences were explored 47% responded with increased stress during mothers' phone distractions. Mother's frequency and attitudes towards device use were also assessed but were unrelated to infant responses. Implications and directions for future research are discussed.

The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build upon. Machine learning algorithms such as k-Nearest-Neighbors were also developed to estimate a mobile device's approximate location inside a building.

El cáncer de piel es el tipo de cáncer más común en todo el mundo. Más de 65.000 personas mueren cada año, por culpa de este cáncer de piel y cada año se diagnostican más de 13 millones de casos de cáncer de piel. En Perú es el cuarto tipo de cáncer con mayor incidencia en el país y anualmente 1200 peruanos son detectados con cáncer de la piel. Las regiones con mayores casos de cáncer de la piel son: Lima 45.9%, La Libertad 13.2%, Lambayeque 7.5%, Cajamarca 4.9%, Junín con 3.1% y otras regiones 25.4% (Sausa, 2017). Además, el 85% de los 130 especialistas oncológicos están concentrados en la capital y en algunas regiones no hay ninguno. En este proyecto se propone un modelo tecnológico para la detección temprana de cáncer de piel. Se busca reducir el tiempo de espera en la obtención de un diagnóstico, además, se ha digitalizado la función del dermatoscopio mediante el uso de Smartphone y lentes de aumento como accesorio del dispositivo móvil. El modelo propuesto está conformado por cinco fases: 1. Entrada. 2. Captura. 3. Canal. 4. Análisis y procesamiento. 5. Visualización. 6. Salida 7. Soporte. Este modelo fue validado en un grupo de 60 pacientes, donde 12 sufren de cáncer piel en etapa tardía, 28 en etapa temprana y 20 son pacientes sanos, en una red de clínicas de Lima, Perú. El resultado obtenido fue 97% de asertividad sobre las lesiones cutáneas analizadas y 90 % en los pacientes sanos (Campos Poma, De La Cruz, Armas-Aguirre, & Gutiérrez González, 2020). / Skin cancer is the most common type of cancer in the world. More than 65,000 people die each year because of this skin cancer and more than 13 million cases of skin cancer are diagnosed each year. In Peru is the fourth cancer with the highest incidence in the country and in the country 1200 Peruvians are detected with skin cancer. major: cases of skin cancer: Lima 45.9%, La Libertad 13.2%, Lambayeque 7.5%, Cajamarca 4.9%, Junin with 3.1% and other regions 25.4% (Sausa, 2017). In addition, 85% of the 130 specialists Oncologists are concentrated in the capital and in some regions, there are none. In this project, we proposed a technological model for the early detection of skin cancer. The purpose is reduce the waiting time to obtaining a diagnosis, in addition, the function of the dermatoscope has been digitized by using a Smartphone and magnifying lenses as an accessory the mobile device. The proposed model has five phases: 1) Input. 2) Screenshot. 3) Channel. 4) Analysis and processing. 5) Display. 6) Ouput. 7) Support. This model was validated in a group of 60 patients, where 12 suffer from skin cancer int the late stage, 28 un the early stage and 20 are healthy patients, in a network of clinics in Lima, Peru. The obtained result was 97% of assertiveness on the analyzed skin lesions and 90% in healthy patients (Campos Poma, De La Cruz, Armas-Aguirre, & Gutiérrez González, 2020). / Tesis

Innovative mobile Endgeräte und Applikationen durchdringen nicht mehr nur das Berufs- und Privatleben, sondern finden auch zunehmend Einsatz in der Aus- und Fortbildung. Dies gilt sowohl in Hinblick auf das „Lifelong Learning“ als auch in der schulischen und universitären Ausbildung. Neue Regelungen zu Bachelor- und Masterprogrammen, Lehrformen wie Gruppenarbeiten und die zunehmende Mobilität der Studierenden verlangen nach neuen Lernmedien und Lehrkonzepten, die es ermöglichen, bisherige Lehrlaufzeiten im Studium sinnvoll zu nutzen. (...)

Internet of Things, or the infrastructure of the information age society, is an expanding area of research. It is the idea that physical things that we have today, can be turned into devices by being connected to a network. The purpose of these devices is through more sensors and actuators, allow smarter and more robust infrastructure to be built, as well as improving consumer devices interoperability. This thesis will investigate the current state of the art application layer communication protocols that have been developed with resource constrained mobile devices in mind. It will look specifically at high latency mid-range wireless cellular communication by investigating how the protocols affect energy consumption for an embedded Internet of Things device. This is done with the constraint that these findings needs to be applicable to the fifth generation of cellular networks, as well as extensions to current generation standards. The application layer communication protocols Constrained Applications Protocol(CoAP), Message Queue Telemetry Transport(MQTT) and Hypertext Transfer Protocol(HTTP) used on multiple modem modules, are investigated in a quantitative approach in deductive survey experiments, testing a variety of what is concluded to be typical sensor and actuator scenarios. The goal with the survey, is to ultimately select the protocol that shows to be the most suitable for a project to build a prototype platform for embedded mobile devices in the shape of publicly shared bikes in a city. This prototype is described and its architecture and design decisions are presented and argued for. The survey discovers that CoAP is the most suitable protocol for the purpose of the thesis, and proceeds to show how it is applied to the described platform.

Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each algorithm for monitoring CVD is also discussed. Both models were able to classify CVD risk with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, unlike current systems the resource requirements for each component in the system was evaluated. The MLP was found to be more efficient when running on the mobile device compared to the SVM. The results of thesis also show that the MLAs complexity was not a barrier to deployment on a mobile device. / Thesis / Master of Applied Science (MASc) / In this thesis, a novel mobile system for monitoring cardiovascular (CVD) disease is presented. The system allows for the continuous monitoring of both physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a remote server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each MLA for monitoring CVD is also discussed. Both models were able to classify CVD severity with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, the resource requirements for each component in the system were evaluated. The results show that the MLAs complexity was not a barrier to deployment on a mobile device.

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