One challenge associated with developing location-based social applications for iOS devices is building a framework on top of Apple’s Core Bluetooth Framework to drive user discovery. Many applications on Apple’s App Store use Bluetooth to enable location-based user discovery. These Social Location-Based Frameworks are private and often are lacking. An ideal Social Location-Based Bluetooth Framework would be public, would be responsive while the application is minimized, have a light battery footprint, and securely transfer the necessary data to enable social interaction. Samantha, a Social Location-Based Framework, meets all of these characteristics. In a Test Application, Samantha took no more than 5 seconds to start up and discover all nearby users. The average discovery time was 3.5 seconds. In addition the battery draw, measured using Apple’s Battery Monitoring Instrument, during testing never exceed 1/20. This means an application running Samantha in the background for a 12-hour period would not significantly drain the battery. In terms of security, Samantha transfers a Unique User Identifier (UUID) across Bluetooth than contains no sensitive information. This UUID, a string of random characters, contains no personal information and it is only useful because it allows specific identification of a nearby user in a database holding additional information. This two-step process ensures that confidential information is never exposed. An example application, Ripple, uses Samantha to create a location-based social application and highlights the framework’s intended use.

This thesis is focused on development of framework for spatial data management. As proof of the concept, an Objective-C application based on the framework was developed. Further, Core Data framework including theoretical and practical functions for development is introduced. Research part of thesis deals with open source library SpatiaLite and their useful functions.

vii, 75 leaves : ill. ; 28 cm Typescript. (Another copy on microfilm is located in Archives) Thesis (M.S.) -- University of Oregon Includes vita and abstract Bibliography: leaves 68-75 University of Oregon theses, Dept. of Biology, M.S., 1980

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abstract: Due to the shortcomings of modern Mobile Device Management solutions, businesses have begun to incorporate forensics to analyze their mobile devices and respond to any incidents of malicious activity in order to protect their sensitive data. Current forensic tools, however, can only look a static image of the device being examined, making it difficult for a forensic analyst to produce conclusive results regarding the integrity of any sensitive data on the device. This research thesis expands on the use of forensics to secure data by implementing an agent on a mobile device that can continually collect information regarding the state of the device. This information is then sent to a separate server in the form of log files to be analyzed using a specialized tool. The analysis tool is able to look at the data collected from the device over time and perform specific calculations, according to the user's specifications, highlighting any correlations or anomalies among the data which might be considered suspicious to a forensic analyst. The contribution of this paper is both an in-depth explanation on the implementation of an iOS application to be used to improve the mobile forensics process as well as a proof-of-concept experiment showing how evidence collected over time can be used to improve the accuracy of a forensic analysis. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Denna rapport beskriver utvecklingen av en iOS applikation för att undersöka resursanvändningen vid rendering av 3D-objekt med och utan “Level of Detail” (LoD). Uppdragsgivaren är företaget ÅF i Karlstad. Vid körning av applikationen renderas ett högt antal 3D-objekt och utefter kamerans bana samlas mätdata in. Mätdatan, i form av resursanvändning av CPU och RAM samt FPS-prestanda, presenteras och analyseras i slutet av denna rapport. Våra resultat visar att med LoD krävs det mindre RAM, vilket innebär att mer komplexa 3D-objekt kan hanteras, men den visuella kvaliteten på dessa renderade 3D-objekt försämras något. / This report describes the development of an iOS application, to examine the usage of resources when rendering 3D-objects with and without ‘Level of Detail’ (LoD). The task was given to us by the company ÅF in Karlstad. Upon launch, the application renders a large number of 3D-objects, and measurement data is collected along the camera’s path. The data, which is about CPU and RAM usage and FPS performance, is presented and analyzed at the end of this report. Our results show a decrease of RAM usage when LoD is activated, which means that more complex 3D-objects can be handled, but the visual quality of these 3D-objects is slightly impaired.

Master of Science / Department of Computer Science / Antonio R. Asebedo / Mitchell L. Neilsen / Small unmanned aircraft systems (sUAS) have been in commercial use since the1980’s and over 8-12% of its current uses are in the agricultural sector, but only involving limited uses like surveying, mapping and imaging, which is expected to increase to 47% according to AUVSI with the association of Artificial Intelligence over the next decade. Our research is one such effort to help farmers utilize advanced sUAS technology coupled with Artificial Intelligence and give them meaningful results in a widely used and user friendly interface, like a mobile application. The vision for this application is to provide a completely automated experience to the farmer for a repetitive and periodic analysis of his/her crops where all the instruction needed from the farmer is a push of a button on a one time configured application and ultimately providing results in seconds. This would help the farmer scout their crops, assess yield potential, and determine if additional inputs are needed for increasing grain yield and profit per acre. For making this application we focused on user-friendliness by abstracting crop algorithms, minimized necessary user inputs, and automate the construction of flight paths. Due to internet connection not always being available at farm fields, processing was kept to on-board compute systems and the mobile device to give live results to farmers without reliance on cloud-based analytics. The application is configured to work with DJI Aircraft using OpenCv for video processing and mobile vision, GIS and GPS data for accurate mapping, locating device, sUAS on the mobile application, and FFMPEG for encoding and decoding compressed video data. An algorithm developed by Precision-Ag Lab at the K-State Agronomy Department was implemented into the sUAS application for providing real time yield estimations and nitrogen recommendation algorithm for winter wheat.

This diploma thesis is focused on proposal and actual creation of mobile application to support in-house processes, especially for improving ordering and delivery processes inside of small company. Thesis includes analysis of current state of processes and analysis of client’s requirements. Based on those requirements, mobile application is created, built with Ionic and Angular framework.

The work deals with design and implementation of application for IOS platform using modern programming tools such as cocos2D framework and Xcode.

This masther's thesis aims at familiarization with the development of mobile application that can control intelligent electrical installation iNELS and its multimedia superstructure iMM. The thesis also describes the common development for the iOS platform and explore the possibility of communication with both controlled systems. Two important communication protocols will be described - EPSNET and XML-RPC. The resulting product is evaluated at the end and its behavior in full operation is described too.