Centrosomin (Cnn) is an integral centrosomal protein in Drosophila with orthologues in several species, including humans. The human orthologue of Cnn is required for brain development with Cnn hypothesised to play a similar role in Drosophila. Control of Cnn incorporation into centrosomes is crucial for controlling asymmetric division in certain types of Drosophila stem cells. FRAP experiments on Cnn show that Cnn recovers in a pe- culiar fashion, which suggest that Cnn may be incorporated closest to the centrioles and then spread radially outward, either diffusively or ad- vectively. The aim of this thesis is to understand the mechanism of Cnn incorporation into the Drosophila centrosomes, to determine the mode of transport of the incorporated Cnn, and to obtain parameter estimates for transport and biochemical reactions. A crucial unknown in the modelling process is the distribution of Cnn receptors. We begin by constructing coupled partial differential equation models with either diffusion or advection as the mechanism for incorpo- rated Cnn transport. The simplest receptor distribution we begin with involves a spherical, infinitesimally thick, impermeable shell. We refine the diffusion models using the insights gained from comparing the model out- put with data (gathered during mitosis) and through careful assessment of the behaviour of the data. We show that a Gaussian receptor distribution is necessary to explain the Cnn FRAP data and that the data cannot be explained by other simpler receptor distributions. We predict the exact form of the receptor distribution through data fitting and present pre- liminary experimental results from our collaborators that suggest that a protein called DSpd2 may show a matching distribution. Not only does this provide strong experimental support for a key prediction from our model, but it also suggests that DSpd2 acts as a Cnn receptor. We also show using the mitosis FRAP data that Cnn does not exhibit appreciable radial movement during mitosis, which precludes the use of these data to distinguish between diffusive and advective transport of Cnn. We use long time Cnn FRAP data gathered during S-phase for this purpose. We fit the S-phase FRAP data using the DSpd2 profiles gath- ered for time points corresponding to the Cnn FRAP experiments. We also use data from FRAP experiments where colchicine is injected into the embryos to destroy microtubules (since microtubules are suspected to play a role in advective transport of Cnn). From the analysis of all these data we show that Cnn is transported in part by advection and in part by diffusion. Thus, we are able to provide the first mechanistic description of the Cnn incorporation process. Further, we estimate parameters from the model fitting and predict how some of the parameters may be altered as nuclei progress from S-phase to mitosis. We also generate testable predic- tions regarding the control of the Cnn incorporation process. We believe that this work will be useful to understand the role of Cnn incorporation in centrosome function, particularly in asymmetrically dividing stem cells.

To explore strategic narratives of the U.S. and Russia is a motivation for this research. The study investigates whether there is a return to the Cold War rhetoric between the West and Russia, or if the discourse has taken a new form. A primary goal is to examine if media originating from the two countries spread propaganda, but mainly to detect what kind of propaganda it is. The research compares types of propaganda techniques that are most commonly applied by RT and CNN, and discusses results in a context of the Cold War propaganda prominent themes. This has been done by comparing how the two media outlets were reporting on a crash of a Malaysian Airlines aircraft in eastern Ukraine on July 17th 2014. A method of a framing analysis has been applied for a material from both channels for a period of four months. The results indicate that a number of propaganda techniques are used by both RT and CNN. Moreover, channels’ discourse is antagonistic, while strategic narratives of the U.S. and Russia nowadays have similarities and differences comparing to the Cold War times. Further research should look at other genres, events and topics reported by the two media.

After a digital photo has been taken by a camera, it can be manipulated to be more appealing. Two ways of doing that are to reduce noise and to increase the saturation. With time and skills in an image manipulating program, this is usually done by hand. In this thesis, automatic image improvement based on artificial neural networks is explored and evaluated qualitatively and quantitatively. A new approach, which builds on an existing method for colorizing gray scale images is presented and its performance compared both to simpler methods and the state of the art in image denoising. Saturation is lowered and noise added to original images, which the methods receive as inputs to improve upon. The new method is shown to improve in some cases but not all, depending on the image and how it was modified before given to the method.

Alex Krizhevsky and his colleagues changed the world of machine vision and image processing in 2012 when their deep learning model, named Alexnet, won the Im- ageNet Large Scale Visual Recognition Challenge with more than 10.8% lower error rate than their closest competitor. Ever since, deep learning approaches have been an area of extensive research for the tasks of object detection, classification, pose esti- mation, etc...This thesis presents a comprehensive analysis of different deep learning models and architectures that have delivered state of the art performances in various machine vision tasks. These models are compared to each other and their strengths and weaknesses are highlighted. We introduce a new approach for human head and shoulder detection from RGB- D data based on a combination of image processing and deep learning approaches. Candidate head-top locations(CHL) are generated from a fast and accurate image processing algorithm that operates on depth data. We propose enhancements to the CHL algorithm making it three times faster. Different deep learning models are then evaluated for the tasks of classification and detection on the candidate head-top loca- tions to regress the head bounding boxes and detect shoulder keypoints. We propose 3 different small models based on convolutional neural networks for this problem. Experimental results for different architectures of our model are highlighted. We also compare the performance of our model to mobilenet. Finally, we show the differences between using 3 types of inputs CNN models: RGB images, a 3-channel representation generated from depth data (Depth map, Multi-order depth template, and Height difference map or DMH), and a 4 channel input composed of RGB+D data.

<div> <div> <div> <p>Estimation of physiological vital signs such as the Heart Rate (HR) has attracted a lot of attention due to the increase interest in health monitoring. The most common HR estimation methods such as Photoplethysmography(PPG) require the physical contact with the subject and limit the movement of the subject. Video-based HR estimation, known as videoplethysmography (VHR), uses image/video processing techniques to estimate remotely the human HR. Even though various VHR methods have been proposed over the past 5 years, there are still challenging problems such as diverse skin tone and motion artifacts. In this thesis we present a VHR method using temporal difference filtering and small variation amplification based on the assumption that HR is the small color variations of skin, i.e. micro blushing. This method is evaluated and compared with the two previous VHR methods. Additionally, we propose the use of spatial pruning for an alternative of skin detection and homomorphic filtering for the motion artifact compensation. </p><p><br></p> <p>Intelligent video surveillance system is a crucial tool for public safety. One of the goals is to extract meaningful information efficiently from the large volume of surveillance videos. Person re-identification (ReID) is a fundamental task associated with intelligent video surveillance system. For example, ReID can be used to identity the person of interest to help law enforcement when they re-appear in the different cameras at different time. ReID can be formally defined as establishing the correspondence between images of a person taken from different cameras. Even though ReID has been intensively studied over the past years, it is still an active research area due to various challenges such as illumination variations, occlusions, view point changes and the lack of data. In this thesis we propose a weighted two stream train- ing objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person’s identity. Additionally, we present a camera-aware image-to-image translation method using similarity preserving Star- GAN (SP-StarGAN) as the data augmentation for ReID. We evaluate our proposed methods on the publicly available datasets and demonstrate the efficacy of our methods.</p></div></div></div>

A barcode is the representation of data including some information related to goods, offered for sale, which frequently appears in on-line fashion images. Detecting and decoding barcode has a variety of applications in the on-line marketplace. However, the existing method has limitation in detecting barcode in some backgrounds such as Tassels, strips, and texture in fashion images. So, our work focuses on identifying the barcode region and distinguishing a barcode from its patterns that are similar to it. We accomplish this by adding a post-processing technique after morphological operations. We also apply a Convolutional Neural Network (CNN) to solve this typical object detection problem. A comparison of the performance between our algorithm and a previous method will be given in our results. For decoding part, a package including current common types of decoding scheme is used in our work to decode the detected barcode. In addition, we add a pre-processing transformation step to process skewed barcode images in order to improve the probability of decoding success.

Fantastic progress has been made within the field of machine learning and deep neural networks in the last decade. Deep convolutional neural networks (CNN) have been hugely successful in imageclassification and object detection. These networks can automate many processes in the industries and increase efficiency. One of these processes is image classification implementing various CNN-models. This thesis addressed two different approaches for solving the same problem. The first approach implemented two CNN-models to classify images. The large pre-trained VGG-model was retrained using so-called transfer learning and trained only the top layers of the network. The other model was a smaller one with customized layers. The trained models are an end-to-end solution. The input is an image, and the output is a class score. The second strategy implemented several classical image processing algorithms to detect the individual defects existed in the pictures. This method worked as a ruled based object detection algorithm. Canny edge detection algorithm, combined with two mathematical morphology concepts, made the backbone of this strategy. Sandvik Coromant operators gathered approximately 1000 microscopical images used in this thesis. Sandvik Coromant is a leading producer of high-quality metal cutting tools. During the manufacturing process occurs some unwanted defects in the products. These defects are analyzed by taking images with a conventional microscopic of 100 and 1000 zooming capability. The three essential defects investigated in this thesis defined as Por, Macro and Slits. Experiments conducted during this thesis show that CNN-models is a good approach to classify impurities and defects in the metal industry, the potential is high. The validation accuracy reached circa 90 percentage, and the final evaluation accuracy was around 95 percentage , which is an acceptable result. The pretrained VGG-model reached a much higher accuracy than the customized model. The Canny edge detection algorithm combined dilation and erosion and contour detection produced a good result. It detected the majority of the defects existed in the images.

Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene recognition tasks. Because scene images often contain multiple objects, there may be more useful local semantic information in the convolutional layers of the network, which may be lost in the full connected layers. Therefore, this paper improved the traditional architecture of CNN, adopted the existing improvement which enhanced the convolution layer information, and extracted it using Fisher Vector. Then this thesis introduced the idea of transfer learning, and tried to introduce the knowledge of two different fields, which are scene and object. We combined the output of these two networks to achieve better results. Finally, this thesis implemented the method using Python and PyTorch. This thesis applied the method to two famous scene datasets. the UIUC-Sports and Scene-15 datasets. Compared with traditional CNN AlexNet architecture, we improve the result from 81% to 93% in UIUC-Sports, and from 79% to 91% in Scene- 15. It shows that our method has good performance on scene recognition tasks.

Laser-assisted machining has been considered as an alternative for difficult-to-machine materials such as metallic alloys and ceramics. Machining of some materials such as high chromium alloys and high strength steels is still a delicate and challenging task. Conventional machines or computer numerical control (CNC) machines and cutting tools cannot adapt easily to such materials and induce very high costs for operations of rough machining or finishing. If laser-assisted machining can be implemented successfully for such materials, it will offer several advantages over the traditional methods including longer tool life, shorter machining time and reduced overall costs. This thesis presents the results of the research conducted on laser assisted machining of hard to wear materials used in making heavy duty mineral processing equipment for the mining industry. Experimental set up using a high power Nd:YAG laser beam attached to a lathe has been developed to machine these materials using cubic boron nitride (CBN) based cutting tools. The laser beam was positioned so that it was heating a point on the surface of the workpiece directly before it passed under the cutting tool. Cutting forces were measured during laser assisted machining and were compared to those measured during conventional machining. Results from the experiments show that with the right cutting parameters and laser beam position, laser assisted machining results in a reduction in cutting forces compared to conventional machining. A mathematical thermal model was used to predict temperatures within the workpiece at depths under the laser beam spot. The model was used to determine the effect of various cutting and laser parameters on the temperature profile within the workpiece. This study shows that laser assisted machining of hard to wear materials such as high chromium white cast iron shows potential as a possible economical alternative to conventional machining methods. Further research is needed before it can be introduced in industry as an alternative to conventional machining.

<p>Författare: Magnus Gustafsson Niclas Hagel</p><p>Handledare: Thomas Knoll</p><p>Examinator: Martin Danielsson</p><p>Titel: Al-Jazeera och CNN - En jämförande fallstudie i krigsjournalistik</p><p>Typ av rapport: C - uppsats</p><p>Ämne: Medie- och Kommunikationsvetenskap</p><p>År: Höstterminen 2008</p><p>Sektion: Sektionen för Hälsa och Samhälle</p><p>Syfte: Vårt syfte är att studera och jämföra al-Jazeeras och CNN:s</p><p>bevakning av en händelse i Afghanistankonflikten för att kunna</p><p>redogöra för eventuella skillnader. Vi vill se hur olika faktorer</p><p>påverkar journalistiken. En analys ur ett genusperspektiv</p><p>kommer också att göras.</p><p>Metod: Fallstudie har tillämpats som huvudsaklig metod och vid analys</p><p>av material har innehållsanalys och kritisk diskursanalys använts.</p><p>Slutsatser: Efter att ha jämfört de två nyhetskanalerna kan vi tydligt se att</p><p>det finns stora skillnader i rapporteringen av ett amerikanskt</p><p>flyganfall mot en afghansk by. CNN som amerikansk</p><p>nyhetskanal visar att deras rapportering påverkas av det</p><p>amerikanska medieklimatet där en neutral krigsrapportering kan</p><p>ses som stötande och journalister ständigt utsätts för</p><p>påtryckningar. Ur ett genusperspektiv ser vi dock tydliga</p><p>likheter mellan kanalerna.</p>