Browsing by Author "Flores, Huber, juhendaja"
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Item Autonomous Monitoring of Litter using Sunlight(Tartu Ülikool, 2021) Olapade, Mayowa Olaide; Flores, Huber, juhendaja; Tartu Ülikool. Füüsika instituut; Tartu Ülikool. Loodus- ja täppisteaduste valdkondItem Detecting User Reading Behaviour Using Smartphone Sensors(Tartu Ülikool, 2015) Ilmjärv, Taavi; Srirama, Satish, juhendaja; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja tehnoloogiateaduskond; Tartu Ülikool. TehnoloogiainstituutThere are many different ways to rate mobile content in the form of various explicit user feedback e.g. like buttons, thumbs up and thumbs down, star ratings as well as there are ways to analyse usage statistics of applications on using mobile analytics tools. Implicit feedback enables to collect more data for getting better insight of content usage and user behaviour. In recent years many works have been conducted in order to classify activities using smartphones. Previous works have shown that sensor-based activity recognition on smartphones is feasible. Yet previous works have not classified reading activity on smartphones. This work proposes one possible way to classify this activity with high accuracy. Classifying reading activity provides possibility to have more precise estimates on mobile content usage statistics, by utilizing sensorand visual-based activity recognition techniques. A set of mobile applications was developed to facilitate data collection and labelling. Accelerometer and gyroscope data was collected from 35 different subjects, after cleaning data 4438 sample readings were left. A neural network was trained on 80% of data and 94% accuracy was reached on classifying reading activity using a smartphone. The results show that classifying reading activity using accelerometer and gyroscope data is possible with high degree of accuracy. We provide Android application source code along with neural network training implementation accompanied by training data in a Git repositoryItem Exploiting Human-emitted Thermal Radiation(Tartu Ülikool, 2021) Emenike, Hilary Somtochukwu; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituutWaste is a growing concern for global sustainability, affecting everything from economic development to human health. Effective waste management is critical for mitigating the harmful effects of waste accumulation on society. In this thesis, we develop MIDAS, an innovative sensing approach for effective household waste management. MIDAS builds on the fact that people need to touch objects when they are throwing them away and that these interactions result in thermal footprints on the objects’ surface. By examining the dissipation of the respective thermal footprints, it is possible to identify the waste material and support effective sorting and recycling practices. We validate our approach through extensive empirical benchmarks, demonstrating that MIDAS can recognize a wide range of materials and generalize variations in the people interacting with objects. Our solution paves towards improved sustainability by offering an innovative solution for classifying materials and optimizing household waste management practices.Item Exploring Group Mobility(Tartu Ülikool, 2021) Raza, Ali; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituutHuman mobility research is gaining traction, opening up new avenues for research in smart cities infrastructure development. Human mobility can reveal information regarding population dispersion across a large area, human behaviour, and individuals’ daily routines. In the meantime, individual mobility is intensively investigated, but group mobility is mostly unexplored. In this thesis, we developed user trajectories based on the dataset collected from their cellular network connectivity. We develop a method for assessing group mobility by examining the similarity in users’ trajectories between locations in different users’ routines. Our results indicate that while multiple groups can be formed in specific locations (in a static manner), it is difficult to find users that share the same spatial and temporal characteristics while moving.Item Migration of native Android applications to HTML5(Tartu Ülikool, 2012) Petšjonkin, Oleg; Srirama, Satish, juhendaja; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituutItem Modelling Energy Consumption using Thermal Imaging(Tartu Ülikool, 2022) Hashimov, Hashim; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituutWe contribute a novel sensing solution that can estimate the energy consumption of applications running in personal devices, such as smart and wearable devices, without requiring the instrumentation of the device itself. Energy estimation of applications running in these devices has become a complex challenge. Indeed, personal devices no longer have a detachable battery, such that their design can be further optimized to adjust better to the everyday activities of users. This in turn, it makes difficult to profile the energy consumption of software developed for these devices with typical solutions, like the Monsoon power meter. Our solution uses thermal imaging to derive energy consumption measurements while the applications are running in the device. We develop a functional prototype to demonstrate the feasibility of our solution. Through rigorous benchmarks that take into consideration different applications, we found that our solution can produce estimations of energy consumption that are comparable to existing hardware solutions. We also found that these estimations also capture differences between different types of applications. In addition, we also found that it is possible to use our solution to estimate the energy consumption of applications running in IoT devices. We also developed a regression model that predicts the estimated power from thermal imaging.Item Produce Quality and Pesticide Residue Estimation Using Light Sensing(Tartu Ülikool, 2023) Rao, Karina; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituutWhile produce quality estimation across various stages in the value chain is essential to tackle food loss and waste, determining pesticide residue in fresh produce can alleviate the threat to human health and the environment. Light sensing offers a non-invasive and cost-effective method to establish unique fingerprints for fresh produce. During a 12-day produce decomposition period, it was established that light reflectivity is effective for the quality estimation of vegetables. The AdaBoost classification model with blue light reflectivity value, vegetable items and luminosity as input features achieved a performance accuracy of 92.4%. While measuring reflectivity intensity, it is important to account for varying lighting conditions (luminosity). Notwithstanding the success of predicting the quality of fresh produce, light sensing failed in pesticide residue estimation.Item Quantifying the impact of human touch to estimate quality of fresh fruits using light sensing(Tartu Ülikool, 2023) Odetoyinbo, Ayodeji Pelumi; Flores, Huber, juhendaja; Tartu Ülikool. Füüsika instituut; Tartu Ülikool. Loodus- ja täppisteaduste valdkondItem Revisiting Group Mobility Modelling: A Systematic Evaluation(Tartu Ülikool, 2021) Shahzad, Mubashar; Flores, Huber, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut