The modern cars have been equipped with advanced technical features to help make driving faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state. Driving behavior-based and physiological parameters-based approaches are the two commonly used approaches to monitor driver’s health and mental state. Previously, physiological parameters-based approaches using sensors are often attached to the human body. Although these sensors attached with body provide excellent signals in lab conditions it can often be troublesome and inconvenient in driving situations.  So, physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring. This thesis report presents an intelligent non-contact-based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as a non-contact and pervasive methods for measuring physiological parameters. The contribution of this thesis is in three folds: 1) Implementation of a camera-based method to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering several challenging conditions e.g. illumination, motion, vibration and movement. 2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position extraction from a driving simulator. 3) Investigation of three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify driver’s cognitive load. Here, according to the results, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and the reference sensor. For IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. For cognitive load classification, two separate studies are conducted, study1 with 1-back task and study2 with 2-back task and both time domain and frequency domain features are extracted from the facial videos. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment considering other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc. / SafeDriver:

This thesis is a pilot study on sound as feedback to motion in active video games. The subject addressed herein has been approached from a multidisciplinary perspective drawing from several fields as audio, film and computer science. The study was set up as a user experience study. Participants where exposed to two videos of identical gameplay, one with feedback sound turned on and a second with feedback sound turned off. Each video was followed by a questionnaire and the study was ended by a semi-structured interview. The main aim was to answer whether users preferred gameplay with feedback sound turned on or off. Results of main question were slightly in favour of sound scenario with feedback sound off even thou not statistically significant. The study also highlights several issues with implemented feedback sound (a secondary goal of the study). Main finding here was problem with perceiving sounds intended function. Even thou results can be somewhat discouraging one should see this study in the light of it being a pilot study. The main value and contribution of this study can be found in lessons learned for future implementations.

Detta dokument beskriver ett kandidatexamensarbete som har genomförts av sju civilingenjörsstudenter från Tekniska högskolan vid Linköpings universitet. Syftet med arbetet var att utveckla en smart inpasseringslösning för företaget Zoezi AB och deras produkt Gymsystem. Systemet skulle dels bestå av en enkortsdator som styr ett lås och kommunicerar med Gymsystems servrar samt en mobilapplikation som kommunicerar med denna enkortsdator. Den första delen av detta dokument beskriver hur utvecklingen av detta system gick till, hur väl de krav som ställts har uppfyllts samt gruppens gemensamma erfarenheter från att utveckla mjukvara i ett större projekt. Rapporten beskriver hur en sådan lösning kan implementeras så att den skapar värde för kunden, vilka erfarenheter som kan vara intressanta för framtida projekt, vilket stöd en systemanatomi kan ge, hur Arduinoplattformen kan användas för hårdvaruprototyper, vilket stöd Extreme Programming kan ge, samt hur fördelningen av hållbarhetsdimensioner i ett sådant projekt kan se ut. Följande slutsatser kan dras utifrån projektet: Det implementerade systemet som beskrivs i rapporten skapar värde för kunden. Agila utvecklingsmetoder rekommenderas varmt inför framtida projekt. En systemanatomi ger bra stöd vid aktivitetsformulering. Arduinoplattformen är en bra plattform för hårdvaruprototyper men innebär vissa prestandabegränsningar. Extreme Programming ger bra stöd för en sådan projektgrupp som beskrivs i rapporten. Slutligen kan en skev fördelning av hållbarhetsdimensioner förväntas om dessa inte behandlas medvetet i projektets tidigare faser. Dokumentet senare del består av ett antal bilagor som innehåller individuella utredningar skrivna av gruppens medlemmar. Dessa bilagor utreder olika ämnen som i någon form har anknytning till antingen arbetet i detta projekt eller till mjukvaruprojekt i allmänhet.

No description available.

The harsh radiation environment of space is known to cause bit flips in computer memory. The conventional way to combat this is through error detection and correction (EDAC) circuitry, but for low-budget space missions software EDAC can be used. One such mission is the KTH project Miniature Student Satellite (MIST), which aims to send a 3U CubeSat into low-Earth orbit. To ensure a high level of data reliability on board MIST, this thesis investigates the performance of different types of EDAC algorithms. First, a prediction of the bit flip susceptibility of DRAM memory in the planned trajectory is made. After that, data reliability models of Hamming and Reed-Solomon (RS) codes are proposed, and their respective running times on the MIST onboard computer are approximated. Finally, the performance of the different codes is discussed with regards to data reliability, memory overhead, and CPU usage. The findings of this thesis suggest that using an EDAC algorithm would greatly increase the data reliability. Among the codes investigated, three good candidates are RS(28,24), RS(196,192) and RS(255,251), depending on how much memory overhead can be accepted. / Rymdens strålningsmiljö är känd för att orsaka bitflippar i datorminnen. Vanligtvis motverkas detta genom att felrättande hårdvara installeras på satelliten, men för lågkostnadssatelliter kan rättningen istället skötas i mjukvaran. Ett exempel på en sådan satellit är KTH-projektet Miniature Student Satellite (MIST), vars mål är att skicka upp en 3U CubeSat i låg omloppsbana. Den här uppsatsen undersöker hur olika felrättningsalgoritmer kan användas för att skydda data ombord på satelliten från att bli korrupt. Först görs en uppskattning av hur strålningskänsliga DRAM minnen är i den planerade omloppsbanan. Därefter föreslås datakorruptionsmodeller för Hamming- och Reed-Solomonkoder (RS) tillsammans med en uppskattning av deras respektive körtider på satellitens omborddator. Slutligen diskuteras de föreslagna koderna med hänsyn till datakorruptionsskydd, minnesanvändning och processoranvändning. Uppsatsens slutsats indikerar att användandet av felrättningsalgoritmer kraftigt minskar risken för datakorruption. Bland de koder som undersökts framstår RS(28,24), RS(196,192) och RS(255,251) som de bästa alternativen, beroende på hur mycket extra minnesanvändning som är acceptabelt.

This thesis intends to determine the extent to which machine learning can be used to solve the regression test prioritization (RTP) problem. RTP is used to order tests with respect to probability of failure. This will optimize for a fast failure, which is desirable if a test suite takes a long time to run or uses a significant amount of computational resources. A common machine learning task is to predict probabilities; this makes RTP an interesting application of machine learning. A supervised learning method is investigated to train a model to predict probabilities of failure, given a test case and a code change. The features investigated are chosen based on previous research of history- based and modification-based RTP. The main motivation for looking at these research areas is that they resemble the data provided by Spotify. The result of the report shows that it is possible to improve how tests run with RTP using machine learning. Nevertheless, a much simpler history- based approach is the best performing approach. It is looking at the history of test results, the more failures recorded for the test case over time, the higher priority it gets. Less is sometimes more.

Context. With the increasing demand for image processing applications in multimedia applications, the importance for research on image quality assessment subject has received great interest. While the goal of Image Quality Assessment is to find the efficient Image Quality Metrics that are closely relative to human visual perception, from the last three decades much effort has been put by the researchers and numerous papers and literature has been developed with emerging Image Quality Assessment techniques. In this regard, emphasis is given to Full-Reference Image Quality Assessment research where analysis of quality measurement algorithms is done based on the referenced original image as that is much closer to perceptual visual quality. Objectives. In this thesis we investigate five mostly used Image Quality Metrics which were selected (which includes Peak Signal to Noise Ratio (PSNR), Structural SIMilarity Index (SSIM), Feature SIMilarity Index (FSIM), Visual Saliency Index (VSI), Universal Quality Index (UQI)) to perform an experiment on a chosen image dataset (of images with different types of distortions due to different image processing applications) and find the most efficient one with respect to the dataset used. This research analysis could possibly be helpful to researchers working on big image data projects where selection of an appropriate Image Quality Metric is of major significance. Our study details the use of dataset taken and the experimental results where the image set highly influences the results.  Methods. The goal of this study is achieved by conducting a Literature Review to investigate the existing Image Quality Assessment research and Image Quality Metrics and by performing an experiment. The image dataset used in the experiment is prepared by obtaining the database from LIVE Image Quality Assessment database. Matlab software engine was used to experiment for image processing applications. Descriptive analysis (includes statistical analysis) was employed to analyze the results obtained from the experiment. Results. For the distortion types involved (JPEG 2000, JPEG compression, White Gaussian Noise, Gaussian Blur) SSIM was efficient to measure the image quality after distortion for JPEG 2000 compressed and white Gaussian noise images and PSNR was efficient for JPEG compression and Gaussian blur images with respect to the original image.  Conclusions. From this study it is evident that SSIM and PSNR are efficient in Image Quality Assessment for the dataset used. Also, that the level of distortions in the image dataset highly influences the results, where in our case SSIM and PSNR perform efficiently for the used database.

No description available.

Denna studie behandlar utvecklingen av de internetrelaterade bedrägerierna i Sverige och hur de har utvecklats i samband med utvidgningen av informationssamhället och den eskalerade användningen av internet. Syftet med studien är att undersöka hur de internetrelaterade bedrägerierna har utvecklats sedan en bit in på 2000-talet då användningen av internet fått en allt större inverkan på våra vardagliga liv. Studien har även för avsikt att undersöka hur kriminellas tankegångar gällande bedrägeribrott kan tänkas ha ändrats i samband med den ökande internetanvändningen. För att uppnå syftet med arbetet och besvara dess frågeställningar har en genomgång av tidigare forskning gällande internetrelaterade bedrägerier och internetutvecklingen i Sverige genomförts. Som underlag för internetbedrägeriernas statistiska utveckling ligger den svenska kriminalstatistiken. För att komplettera statistiken har en semistrukturerad elitintervju med en polisinspektör genomförts. Detta för att ur en skiljaktig vinkel ge en fördjupad bild av vad statistiken faktiskt visar. Studiens resultat analyseras sedermera med hjälp av tidigare forskning och rutinaktivitetsteorin Undersökningen visar att de internetrelaterade bedrägerierna under en tid präglats av en uppåtgående trend samt att denna trend med största sannolikhet kommer att fortsätta öka tills vi bekantat oss ytterligare med informationssamhället och dess tekniker. Undersökningen visaräven att det förhöjda internetanvändandet påverkar och bidrar till ökningen av internetbedrägerier. I och med utvecklingen av informationssamhället har även förutsättningarna för bedragarna blivit allt mer gynnsamma då kontroll och lagstiftning inte följer med utvecklingen i samma takt.

VizzAnalyzer is stand-alone tool for analyzing and visualizing the structures of largesoftware systems. Currently, it only supports to analyze Java and UML programs.Considering about the widen acceptance of C/C++ program languages, we think it isnecessary to create this C/C++ Front-End to enable VizzAnalyzer to analyzer C/C++programs.To create the C/C++ Front-End, we need to get C/C++ Front-End Meta-Model first.For doing this, we selected Eclipse CDT as the compiler for C/C++ source files.Secondly, we create a mapping between C/C++ Front-End Meta-Model to Common-Meta-Model. The mapping result will be used by VizzAnalyzer to do further analysiswork.This Bachelor thesis documents relative theory to this C/C++ Front-End and how ithas been developed and implemented.