407 research outputs found

    Down to earth: everyday uses for European space technology

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    A lifelogging system supporting multimodal access

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    Today, technology has progressed to allow us to capture our lives digitally such as taking pictures, recording videos and gaining access to WiFi to share experiences using smartphones. People’s lifestyles are changing. One example is from the traditional memo writing to the digital lifelog. Lifelogging is the process of using digital tools to collect personal data in order to illustrate the user’s daily life (Smith et al., 2011). The availability of smartphones embedded with different sensors such as camera and GPS has encouraged the development of lifelogging. It also has brought new challenges in multi-sensor data collection, large volume data storage, data analysis and appropriate representation of lifelog data across different devices. This study is designed to address the above challenges. A lifelogging system was developed to collect, store, analyse, and display multiple sensors’ data, i.e. supporting multimodal access. In this system, the multi-sensor data (also called data streams) is firstly transmitted from smartphone to server only when the phone is being charged. On the server side, six contexts are detected namely personal, time, location, social, activity and environment. Events are then segmented and a related narrative is generated. Finally, lifelog data is presented differently on three widely used devices which are the computer, smartphone and E-book reader. Lifelogging is likely to become a well-accepted technology in the coming years. Manual logging is not possible for most people and is not feasible in the long-term. Automatic lifelogging is needed. This study presents a lifelogging system which can automatically collect multi-sensor data, detect contexts, segment events, generate meaningful narratives and display the appropriate data on different devices based on their unique characteristics. The work in this thesis therefore contributes to automatic lifelogging development and in doing so makes a valuable contribution to the development of the field

    Design for energy-efficient and reliable fog-assisted healthcare IoT systems

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    Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

    Applications of Trajectory Data From the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study

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    Transportation agencies have an opportunity to leverage increasingly-available trajectory datasets to improve their analyses and decision-making processes. However, this data is typically purchased from vendors, which means agencies must understand its potential benefits beforehand in order to properly assess its value relative to the cost of acquisition. While the literature concerned with trajectory data is rich, it is naturally fragmented and focused on technical contributions in niche areas, which makes it difficult for government agencies to assess its value across different transportation domains. To overcome this issue, the current paper explores trajectory data from the perspective of a road transportation agency interested in acquiring trajectories to enhance its analyses. The paper provides a literature review illustrating applications of trajectory data in six areas of road transportation systems analysis: demand estimation, modeling human behavior, designing public transit, traffic performance measurement and prediction, environment and safety. In addition, it visually explores 20 million GPS traces in Maryland, illustrating existing and suggesting new applications of trajectory data

    D1.1 DEMAND ASSESSMENT FRAMEWORK

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    This report proposes the initial draft of the LeADS ADS Framework composed by three major elements; identification and definition of technologies in scope; skills included under those technologies, and definition of job roles, where other skills frameworks are considered for comparison and alignment. The report summarises the first workshop held by the project with external constituencies even though the feedback will be incorporated in the final version of the framework, where the layer of job roles will be completed, and the others revised according to additional input. This framework serves as reference for the next step in LeADS: the assessment of the demand and the supply

    The Role of Geospatial Data in Data Economy

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    This work is a pre-study, and it is intended to produce a report under the guidance of the Ministry of Agriculture and Forestry (MAF) about the role of geospatial data in data economy, especially in a Finnish context. The aim was to review the state-of-the-art and needs regarding geospatial data and positioning in today’s data economy as well as the impact of geospatial data and positioning. Geospatial data has an important role in data economy. The report delves into the technical aspects of data, unveiling the untapped potential of its value and the cross-disciplinary role it serves in multiple industries. Furthermore, the report emphasizes the synergistic-sustainability potential geospatial data has for addressing climate impacts and facilitating more precise environmental monitoring. The subject is multidisciplinary, and therefore it was logical to include a wide variety of perspectives in the report. According to the review of literature and an illustrating case study, there is a need for many kinds of further research related to geospatial data connected to more precise Earth observation, pervasive positioning solutions, value and use of geospatial data in decision-making and resource allocation, measuring the value as well as customizing services and products related to it. The research related to competences needed to use the data, improvement of the use of data as well as the use of environmental performance indicators is needed too

    Let's track! strategies to establish active people tracking in workplaces

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    The action research component is conducted by developing a system that delivers insights into teamwork dynamics, as revealed by tracking the social network interactions that occur within collaborative work environments. I constructed a working prototype that utilised an indoor people tracking system that captures people's movements as they operate within their workspace. It is capable of simultaneously monitoring the progress of multiple cohabitating project teams. Focusing on providing context specific insights, I designed a flexible behaviour model that constructed customised social networks to extract interactions of interest from the tracked data. The visually rich analysis reporting that was layered with contextual cues enabled quick cognition by the intended viewer. The targeted user covers all levels of the organisation from project collaborators to the support personnel and upper management. With this setup, everyone can participate in a data-supported reflective learning process. The original contribution of my research is two-fold. Firstly, the people tracking system and analytics I developed demonstrated the technical capability to provide real time insights to workspace design, project management and human resource management applications. Secondly, through reference to my three case studies, I argue that a user-centric approach is critical for the successful integration and adaptation of people tracking systems and analytics into real world workplace practices
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