Browsing by Subject "Sensors"

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  • Su, Xiang; Liu, Xiaoli; Hossein Motlagh, Naser; Cao, Jacky; Su, Peifeng; Pellikka, Petri; Liu, Yongchun; Petäjä, Tuukka; Kulmala, Markku; Hui, Pan; Tarkoma, Sasu (2021)
    Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.
  • Muiruri, Dennis (Helsingin yliopisto, 2021)
    Ubiquitous sensing is transforming our societies and how we interact with our surrounding envi- ronment; sensors provide large streams of data while machine learning techniques and artificial intelligence provide the tools needed to generate insights from the data. These developments have taken place in almost every industry sector with topics such as smart cities and smart buildings becoming key topical issues as societies seek more sustainable ways of living. Smart buildings are the main context of this thesis. These are buildings equipped with various sensors used to collect data from the surrounding environment allowing the building to adapt itself and increasing its operational efficiency. Previously, most efforts in realizing smart buildings have focused on energy management and au- tomation where the goal is to improve costs associated with heating, ventilation, and air condi- tioning. A less studied area involves smart buildings and their indoor environments especially relative to sub-spaces within a building. Increased developments in low-cost sensor technologies have created new opportunities to sense indoor environments in more granular ways that provide new possibilities to model finer attributes of spaces within a building. This thesis focuses on modeling indoor environment data obtained from a multipurpose building that serves primarily as a school. The aim is to explore the quality of the indoor environment relative to regulatory guidelines and also exploring suitable predictive models for thermal comfort and indoor air quality. Additionally, design science methodology is applied in the creation of a proof of concept software system. This system is aimed at demonstrating the use of Web APIs to provide sensor data to clients that may use the data to render analytics among other insights to a building’s stakeholders. Overall, the main technical contributions of this thesis are twofold: (i) a potential web-application design for indoor air quality IoT data and (ii) an exposition of modeling of indoor air quality data based on a variety of sensors and multiple spaces within the same building. Results indicate a software-based tool that supports monitoring the indoor environment of a building would be beneficial in maintaining the correct levels of various indoor parameters. Further, modeling data from different spaces within the building shows a need for heterogeneous models to predict variables in these spaces. This implies parameters used to predict thermal comfort and air quality are different in varying spaces especially where the spaces differ in size, indoor climate control settings, and other attributes such as occupancy control.
  • Zuniga Corrales, Agustin; Flores, Huber; Nurmi, Petteri (2021)
    We develop an innovative low-cost approach for characterizing fresh produce by repurposing inexpensive commercial-off-the-shelf green light sensors for quality estimation. Our approach has been designed to support all stages of the supply chain while being inexpensive and easy to deploy. We validate our approach through extensive empirical benchmarks, showing that it can correctly distinguish organic produce from nonorganic items, establish unique fingerprints for different produce, and estimate the quality or ripeness of produce. Specifically, we demonstrate that changes in the reflected green light values correlate with the so-called transpiration coefficients of the produce. We also discuss the practicability of our approach and present application use cases that can benefit from our solution.
  • Naaranoja, T.; Golovleva, M.; Martikainen, L.; Berretti, M.; Österberg, K. (2019)
    Single crystal CVD (scCVD) diamond is an attractive material for particle detection in high energy physics for its good time resolution and reported outstanding radiation tolerance. In addition to direct signal loss via charge carrier trapping, polarization effect, caused by non-homogeneous filling of trap defects, is a known cause of signal degradation in irradiated scCVD diamond. This phenomenon was studied by intentionally polarizing irradiated diamonds. Even the relatively lightly irradiated (1014 protons/cm2) diamonds exhibited strong enough polarization to collapse the electric field with moderate rate of 5 MeV alpha particles. The transient current measurements were reproduced with TCAD simulations. The hypothesis that the polarization is caused by single neutral defect type in the bulk, was tested using two generic models. Neither one has a satisfactory agreement with the measurement data, which indicates that trapping at the interfaces play a significant role in space charge polarization.
  • Li, Tong; Zhang, Mingyang; Li, Yong; Lagerspetz, Eemil; Tarkoma, Sasu; Hui, Pan (2021)
    The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users' smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.
  • Makitalo, Niko; Flores-Martin, Daniel; Berrocal, Javier; Garcia-Alonso, Jose; Ihantola, Petri; Ometov, Aleksandr; Murillo, Juan Manuel; Mikkonen, Tommi (2020)
    Today, creating innovative Internet of Bodies solutions requires manually gathering the needed information from an increasing number of services and personal devices. In this article, we tackle this challenge by presenting Human Data Model-a programming framework for combining information from several sources, performing computations over that information to high-level abstractions, and then providing these abstractions to proactively schedule computer-human interactions.