Augmented Reality Browser for Android Smartphones

Master's thesis in Computer scienceAugmented Reality capabilities complement the reality captured by the sensors (human’s senses or technological devices) by adding virtual elements that improve the experience with the real world. The purpose of this project is to develop an application for android smartphones with Augmented Reality (AR) characteristics. Principal characteristic includes an AR view location-based, by using the GPS, camera, and the sensors such as accelerometer, magnetic field, and gyroscope. In addition, the integration of different open data sources that provide relevant information of points of interest (POI's) like touristic places, natural locations, and local business. Based on the searching filters and coordinates of current position. For a better understanding, the mathematical foundation will be explained (three axes space x, y and z) for calculate positioning of markers that will be projected in the augmented reality view, by processing and filtering the data provided by the sensors, as well, an explanation of the libraries used to develop the different components of the application. After this, a revision to the accuracy of sensor methods for capture the localization and motion changes, by using low/high pass filters in the signals and fusion of sensed data, to cancel noise and drift, and get more accurate orientation vectors. The results of this project are presented in form of an application with AR capabilities, such that it can be used for smart and interactive browsing, and to understand the basis of AR concept applied in a real use case. Further research, in this project is to present additional helps in the dashboard by using image recognition, for interact with the objects around, and contextualized searching based on filtering history and user preferences

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

Acoustic Sensing: Mobile Applications and Frameworks

Acoustic sensing has attracted significant attention from both academia and industry due to its ubiquity. Since smartphones and many IoT devices are already equipped with microphones and speakers, it requires nearly zero additional deployment cost. Acoustic sensing is also versatile. For example, it can detect obstacles for distracted pedestrians (BumpAlert), remember indoor locations through recorded echoes (EchoTag), and also understand the touch force applied to mobile devices (ForcePhone). In this dissertation, we first propose three acoustic sensing applications, BumpAlert, EchoTag, and ForcePhone, and then introduce a cross-platform sensing framework called LibAS. LibAS is designed to facilitate the development of acoustic sensing applications. For example, LibAS can let developers prototype and validate their sensing ideas and apps on commercial devices without the detailed knowledge of platform-dependent programming. LibAS is shown to require less than 30 lines of code in Matlab to implement the prototype of ForcePhone on Android/iOS/Tizen/Linux devices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143971/1/yctung_1.pd

Options and recommandations related to further development of an Espon Cartographic Language

In this 5th part of Espon Cartographic Language Final Report, our aim is to identify good practices, as well in the development of interactive cartographic environments such as atlases, as in innovative cartographic constructions. Our proposals target several levels:- The level of applications themselves: which functionalities have to be use, for what applications and what objectives?-The level of cartographic representations, meaning the possibilities to introduce elements of animation and interactivity in maps, depending on data and objectives: what innovations for which representation?To achieve such aims, we use two types of resources:- a collection of interactive atlases, considered as the most representative of the diversity in european statistical atlases, which we have analyzed and compared.- the collection of maps presented in Task 4, that we propose to enrich with functions of interaction and animation.The first part of Task 5 deals with recommendations, coming from a comparative analysis of european statistical atlases. These recommendations depend on the type of environment to be made (environment of visualization, analysis or exploration), and on the desired interactivity level.The second part deals with recommendations to create interactive and animated maps. They are illustrated by concrete proposals, in the form of summary datasheet.The final part deals with a comparison of computer tools that can be used to make innovative cartographic applications

Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables

This paper discusses the possibility of detecting personal stress making use of popular wearable devices available in the market. Different instruments found in the literature to measure stress-related features are reviewed, distinguishing between subjective tests and mechanisms supported by the analysis of physiological signals from clinical devices. Taking them as a reference, a solution to estimate stress based on the use of commercial-off-the-shelf wrist wearables and machine learning techniques is described. A mobile app was developed to induce stress in a uniform and systematic way. The app implements well-known stress inducers, such as the Paced Auditory Serial Addition Test, the Stroop Color-Word Interference Test, and a hyperventilation activity. Wearables are used to collect physiological data used to train classifiers that provide estimations on personal stress levels. The solution has been validated in an experiment involving 19 subjects, offering an average accuracy and F-measures close to 0.99 in an individual model and an accuracy and F-measure close to 0.85 in a global 2-level classifier model. Stress can be a worrying problem in different scenarios, such as in educational settings. Thus, the last part of the paper describes the proposal of a set of stress related indicators aimed to support the management of stress over time in such settings.Agencia Estatal de Investigación | Ref. TIN2016-80515-RUniversidade de Vig

Compressed Sensing in Resource-Constrained Environments: From Sensing Mechanism Design to Recovery Algorithms

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of CS in resource-constrained environments. First, we try to solve the problem on how to design sensing mechanisms that could better adapt to the resource-limited smartphone platform. We propose the compressed phone sensing (CPS) framework where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality of the smartphones and the requirement of active user inputs for data collection that may place a high burden on the user. Second, we propose a CS reconstruction algorithm to be used in VSNs for recovery of frames/images. An efficient algorithm, NonLocal Douglas-Rachford (NLDR), is developed. NLDR takes advantage of self-similarity in images using nonlocal means (NL) filtering. We further formulate the nonlocal estimation as the low-rank matrix approximation problem and solve the constrained optimization problem using Douglas-Rachford splitting method. Third, we extend the NLDR algorithm to surveillance video processing in VSNs and propose recursive Low-rank and Sparse estimation through Douglas-Rachford splitting (rLSDR) method for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited