The smartphone is commonly used for image and video capture. Due to the lightweight of the smartphone, small handshakes are noticeable in recorded videos. The image sensors in smartphones are typically CMOS, which introduces rolling shutter artefacts. Stabilisation is introduced to minimise the effects of the camera movement and artefacts. Stabilisation relies on accurate camera parameters. The manufacturer usually gives values to the camera parameters, but calibration is required to obtain the best results. Calibration is performed using a video and the affiliated parameters recorded by the smartphone while the video is recorded. Calibration consists of four components. The first one is feature point detection. Feature points are high contrast visual features that are easily detected. The second is optical flow. Optical flow is used to track the feature points between two consecutive frames. The third is an objective function. The points are stabilised, and the objective function for this case is set as a measurement of how much the stabilised points move. The final component is an optimisation method used to find the minimum of the objective function. In its simplest form, the calibration algorithm detects points in the first frame of a video, tracks them throughout the video, and uses the optimisation to minimise the corresponding objective function. Since the stabilisation depends on the calibration parameters, the calibrated values are obtained as the values that give the lowest objective function value. This method was refined to analyse frames and feature points and calibrate on a subset of a video. Due to the large number of camera units being produced each year, it is infeasible to calibrate every unit individually. Instead, one unit of each type is calibrated, and this calibration is used on all units of that type. One way to reach each unit individually and not rely on a general calibration is to introduce automatic calibration. Every unit would calibrate itself automatically as users record 'normal' videos. However, demands are placed on the calibration video, so it is recorded under controlled circumstances. This project aims to map which video properties can negatively affect the calibration, how these can be detected, and analyse the effects of calibrating on the parts of the video that do not contain these. The critical video properties were expected to be movements in the video, the camera movement, the distance to the feature points, and the feature point amount and spread. An object detection algorithm was used to check for the movement of objects in the video. First, the lack of movement was checked by comparing the relative rotation of consecutive frames. Secondly, the motion blur was analysed in two ways. The primary was to calculate the number of blurred pixels using exposure time and camera rotation. The other way to check for blur was to check the exposure time. Next, feature points located on objects close to the camera were eliminated by checking the relative movements of the feature points and removing the feature points that move more than others. To check for the feature point amount, a strict limit was placed. Finally, to check for the spread, the frame was divided into boxes, and a limit was placed on the number of points per box. The frame and feature point elimination methods were evaluated by letting three people record 'normal' user videos. Calibration was performed using reference calibration, which was not to remove any frames or feature points, and different frame and feature point elimination methods. The obtained calibrated values were evaluated. Some frame removal methods are slightly better than the reference calibration. However, it does not successfully eliminate all bad frames or manage to keep all good frames, so more efficient limits might need to be implemented. Only a subset of the 'normal' videos was sufficiently good to calibrate on. After improvements, adaptation to real-time implementation on a smartphone would be the next major step in obtaining real-time calibration on a smartphone.

Despite of all the advances in smartphone technology in recent years, smartphones still remain limited by their battery life. Unlike other power hungry components in the smartphone, the cellular data and Wi-Fi interfaces often continue to be used even while the phone is in the idle state to accommodate unnecessary data traffic produced by some applications. In addition, bad reception has been proven to greatly increase energy consumed by the radio, which happens quite often when smartphone users are inside buildings. In this paper, we present a Short message service Push based Service (SPS) to save unnecessary power consumption when smartphones are in idle state, especially in bad reception areas. First, SPS disables a smartphone's data interfaces whenever the phone is in idle state. Second, to preserve the real-time notification functionality required by some apps, such as new email arrivals and social media updates, when a notification is needed, a wakeup text message will be received by the phone, and then SPS enables the phone's data interfaces to connect to the corresponding server to retrieve notification data via the normal data network. Once the notification data has been retrieved, SPS will disable the data interfaces again if the phone is still in idle state. We have developed a complete prototype for Android smartphones. Our experiments show that SPS consumes less energy than the current approach. In areas with bad reception, the SPS prototype can double the battery life of a smartphone.

Arbetet behandlar frågan om elever läser olika snabbt beroende på varifrån de läser en text? Studiens grundläggande syfte är att undersöka om det föreligger någon skillnad i läshastighet i en bok, på en dator eller i en så kallad smartphone? Undersökningsgruppen är fem slumpmässigt utvalda flickor i årskurs 8. Intresset för arbetet tar sin grund i ett stort eget intresse för läsning men också i deltagande vid insamlandet av data till en större studie i ämnet. Studien har för avsikt att jämföra lästeknik och förståelse i en bok, på en dator och i smartphones. Som en del två skall försök göras för att utveckla lästekniken genom mental teknik- lånad från idrotten - i avsikt att utveckla elevers läsförmåga och uppfattningsmöjlighet.Metoden som används i är i detta arbete en fallstudie samt en analys utifrån observationer av deltagarna. Dessa filmas när de läser och sen analyseras filmerna utifrån antal ord per minut (opm) från en bok, en dator och en telefon. Metoden är till viss del egendesignad vilket den kan få kritik för men validiteten och reliabiliteten är att betrakta som hög. Förståelse finns med som en variabel i resultat och diskuteras kort i diskussionen för att försäkra att deltagarna verkligen läst. Den är dock inte avsedd att undersökas i jämförelse med källa och hastighet.Resultatet påvisar en snabbare läshastighet generellt för telefonen (28 opm mer) än i en bok eller telefon. Det visar dock också på att boken vinner över telefonen i två av fem fall och att eleverna som läser på telefonen uppvisar en snabbare läshastighet vid samtliga fall jämfört med en dator. Resultaten är att betrakta som "spretiga" och det går därför inte att utläsa säkra data utifrån dem. Undersökningsgruppen är också att betrakta som liten i sammanhanget.Frågor som ställs och behandlas i diskussionen är om elever lär sig mer om de läser snabbare och om det inte handlar mycket om den enskilda individen och vad han eller hon har för förmågor. Mer förbättringar på mobil och datorområdet och läsning görs och kommer att fortsätta göras och det här arbetet skummar på ämnets yta.Läsning är en komplicerad process som innefattar många faktorer och arbetet har gett upphov till insikt i begreppen förståelse, tolkning av ord och tänkande på en högre nivå som används som definitioner av vad läsning egentligen är. Detta gör det således svårt att forska på området om man inte är väldigt specifik i sin inriktning.

The most common navigation aid visually-impaired people employ is a white cane, but, recently, technology has given rise to a varied set of sophisticated navigation aids. While these new aids can provide more assistance to a visually-impaired person than a white cane, they tend to be expensive due to a small market segment, which in turn can reduce their accessibility. In an effort to produce a technologically-advanced yet accessible navigation aid, an Android application is proposed that detects and notifies users about obstacles within their path through the use of a smartphone's camera. While the smartphone is mounted on a harness worn by the user, the Walking Assistant application operates by capturing images as the user walks, finding features of objects within each frame, and determining how the features have moved from image to image. If it is discovered that an object is moving towards the user, the Walking Assistant will activate the smartphone's vibration mode to alert the user to the object's presence. Additionally, the user can control the Walking Assistant through the use of either touch or voice commands. By conducting real-world tests, it was determined that the Walking Assistant can correctly identify obstacles 42.1% of the time, while generating false positive obstacle identifications only 15.0% of the time. The accuracy of the Walking Assistant can be further improved by implementing additional features, such as a fuzzy-decision-based thresholding system or image stabilization.

The objectives of this study were to (1) develop a non-invasive method (referred to as Smart Photo-Tape) to calculate participant-specific upper arm, forearm, and hand segment inertial properties (SIPs) (e.g. mass, center of mass, and radii of gyration) and (2) use those Smart Photo-Tape properties in inverse dynamics (ID) analyses to calculate injury-related pitching arm kinetics. Five 20- to 23- year-old baseball pitchers were photographed holding a baseball and analyzed using the Smart Photo-Tape method to obtain 3-D inertial properties for their upper arm, forearm, and hand. The upper arm and forearm segments were modelled as stacked elliptic cylinders and the hand was modelled as an ellipsoid. One participant received a dual energy x-ray absorptiometry (DXA) scan and conducted a motion analysis study, pitching 10 fastballs. Scaled SIPs from cadaver studies and Smart Photo-Tape SIPs were compared using one sample t-tests. Pitching arm kinetic predictions were calculated and compared using scaled inverse dynamics (ID), Smart Hand ID (a combination of scaled SIPs for the upper arm and forearm and Smart Photo-Tape SIPs for the hand), and Smart Photo-Tape ID. The major result was that the Smart Photo-Tape SIPs were significantly different when compared to their respective scaled inertial properties, with the hand segment producing the largest difference between the scaled SIPs and Smart Photo-Tape SIPs. The implication of this study is that researches or coaches can use the Smart Photo-Tape method to calculate participant specific SIPs for pitching arm kinetic analysis.

With the proliferation of sensor-enabled smartphones, significant attention has been attracted to develop sensing-driven mobile systems. Current research on sensing-driven mobile systems can be classified into two categories, based on the purpose of sensing. In the first category, smartphones are used to sense personal context information, such as locations, activities, and daily habits to enable applications such as location-aware systems and virtual reality systems. In the second category, smartphones are exploited to collect sensing data of the physical world and enable applications including traffic monitoring, environmental monitoring, and others. As smartphones become blossomed in popularity and ubiquity, new problems have emerged in both categories of mobile sensing systems. In this thesis, we investigate three core challenges by answering the following fundamental questions: first, how can we utilize user context to improve the operating system performance? Second, how can we process sensing data, especially images, with high accuracy? Third, how can we enable distributed sensing while satisfy resource constraints of smartphones? The first part of this thesis studies how to exploit user context to improve the responsiveness of mobile operating systems. We propose a context-aware application-preloading engine named FALCON. The core of FALCON is a decision engine that learns application usage patterns of mobile users and preloads applications ahead of time to improve the responsiveness of mobile OS. Compared with other approaches such as caching schemes like Least Recently Used (LRU), Falcon improves the application responsiveness by two times. The second part of this thesis focuses on image search for mobile phones. We first explore how to improve image search accuracy on centralized servers, and propose an image search engine named CrowdSearch. The core idea of CrowdSearch is to incorporate crowdsourced human validation into the system for removing erroneous results from automated image search engines, while still provide realtime response for mobile users. Compared with existing automated image search engines, CrowdSearch achieves over 95% accuracy consistently across multiple categories of images with response time in a minute. We then extend image search to distributed mobile phones, and emphasis resource constraint problems, especially on energy and bandwidth. We propose a distributed image search system named SenSearch, which turns smartphones into micro image search engines. Images are collected, indexed, and transmitted using compact features that are two magnitudes smaller than their raw format. SenSearch improves the energy and bandwidth cost by five times compared with centralized image search engines.

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