This thesis deals with the creation of a system for managing and monitoring the use of mobile devices in a selected company. Firstly, it deals with the analysis of the current state and then, based on the identified requirements, describes the system design and implementation using selected technologies. The system is implemented in the form of an Android application in Java and a web application in the JavaScript framework Vue.js.

This paper introduces a system which enables visually impaired users to detect objects and landmarks within the line of sight. The system works in two modes: landmark mode, which detects predefined landmarks, and object mode, which detects objects for everyday use. Users can get audio announcement for the name of the detected object or landmark as well as its estimated distances. Landmark detection helps visually impaired users explore an unfamiliar environment and build a mental map. The proposed system utilizes a deep learning system for detection, which is deployed on the mobile phone and optimized to run in real-time. Unlike many other existing deep-learning systems that require an Internet connection or specific accessories. Our system works offline and only requires a smart phone with camera, which gives the advantage to avoid the cost for data services, reduce delay to access the cloud server, and increase the system reliability in all environments.

The popularity of smart phones has risen throughout the years since first introduced. With the popularity of the devices growing so too has the number of malicious applications flooding the devices’ marketplaces. With more usage there becomes a larger target for malware and exploitation creation. As threats to these devices continue to grow there is a constant need for security to safeguard against these threats. Some attempts to protect smart phones involve building software to analyze applications running on the devices. This attempt has cut back on the amount of malicious software on the marketplace. These attempts however only catch malicious applications after they have been running. This dissertation presents the Secure Android Development Model. The goal of this model is to contribute to security of these devices by having a development model that implicitly builds security into applications. The model ensures a minimal amount of open permissions thus limiting the number of attack vectors that malicious software can make on the devices. By following the model, developers will have all information available during development to make appropriate security decisions in their applications.

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Construction projects require large amounts of cyber-information, such as 3D models, in order to achieve success. Unfortunately, this information is typically difficult for construction field personnel to access and use on-site, due to the highly mobile nature of the job and hazardous work environments. Field personnel rely on carrying around large stacks of construction drawings, diagrams, and specifications, or traveling to a trailer to look up information electronically, reducing potential project efficiency. This thesis details my work on Hybrid 4-Dimensional Augmented Reality, known as HD4AR, a mobile augmented reality system for construction projects that provides high-precision visualization of semantically-rich 3D cyber-information over real-world imagery. The thesis examines the challenges related to augmenting reality on a construction site, describes how HD4AR overcomes these challenges, and empirically evaluates the capabilities of HD4AR. / Master of Science

Android malware collusion is a new threat model that occurs when multiple Android apps communicate in order to execute an attack. This threat model threatens all Android users' private information and system resource security. Although recent research has made advances in collusion detection and classification, security analysts still do not have robust tools which allow them to definitively identify colluding Android applications. Specifically, in order to determine whether an alert produced by a tool scanning for Android collusion is a true-positive or a false-positive, the analyst must perform manual analysis of the suspected apps, which is both time consuming and prone to human errors. In this thesis, we present a new approach to definitive Android collusion detection and confirmation by rendering inter-component communications between a set of potentially collusive Android applications. Inter-component communications (abbreviated to ICCs), are a feature of the Android framework that allows components from different applications to communicate with one another. Our approach allows Android security analysts to inspect all ICCs within a set of suspicious Android applications and subsequently identify collusive attacks which utilize ICCs. Furthermore, our approach also visualizes all potentially collusive data-flows within each component within a set of apps. This allows analysts to inspect, step-by-step, the the data-flows that are currently used by collusive attacks, or the data-flows that could be used for future collusive attacks. Our tool effectively visualizes the malicious and benign ICCs in sets of proof-of-concept and real-world colluding applications. We conducted a user study which revealed that our approach allows for accurate and efficient identification of true- and false-positive collusive ICCs while still maintaining usability. / Master of Science

Horizontal curves are a contributing factor for numerous deaths on roadways. The curve characteristics dictate the severity of the curve and require the driver to be more attentive while driving. To address this issue, advisory speeds are posted on horizontal curves to warn drivers to slow down for their safety. There are six main procedures to assign advisory speeds. This paper focuses on two of these methods, finds a connection between the two, and develops an Android Application that can be used to determine an advisory speed for a curve. In this work, a brief summary of the six existing methods for advisory speed assignment are discussed. Pros and cons are included for each for comparison purposes. Next, two of these methods are highlighted by applying them in the field. Data is collected and a relationship between them is determined. Using this relationship, an Android Application is created and the various details of the design process are described. This Application, called CurveAdvisor, allows the user to assign the appropriate advisory speed on a desired horizontal curve. An analysis is then conducted to test the effectiveness of CurveAdvisor. Results indicate that CurveAdvisor is successful in many cases. Finally, contributions and suggestions for future work are included. / Master of Science