The topic of this paper is how CNN and BBC, two of the largest media companies in the world, presented Islam in the Palestinian civil war during the years 2006-2007. Articles that CNN and BBC published on the Palestinian civil war have been analyzed in order to answer this question. The purpose is to see if Islam is portrayed in an Islamophobic way by CNN and BBC and if it is possible to find discursive tracks from Clash of Civilizations-theory in the analyzed articles. The findings indicate that there are elements of Islamophobia and discursive tracks of Clash of Civilizations when it comes to presenting islam during the Palestinian civil war. Another conclusion is also that CNN and BBC presented islam in different ways during the civil war.

This thesis presents the results of an architectural study on the design of FPGA- based architectures for convolutional neural networks (CNNs). We have analyzed the memory access patterns of a Convolutional Neural Network (one of the biggest networks in the family of deep learning algorithms) by creating a trace of a well-known CNN architecture and by developing a trace-driven DRAM simulator. The simulator uses the traces to analyze the effect that different storage patterns and dissonance in speed between memory and processing element, can have on the CNN system. This insight is then used create an initial design for a layer architecture for the CNN using an FPGA platform. The FPGA is designed to have multiple parallel-executing units. We design a data layout for the on-chip memory of an FPGA such that we can increase parallelism in the design. As the number of these parallel units (and hence parallelism) depends on the memory layout of input and output, particularly if parallel read and write accesses can be scheduled or not. The on-chip memory layout minimizes access contention during the operation of parallel units. The result is an SoC (System on Chip) that acts as an accelerator and can have more number of parallel units than previous work. The improvement in design was also observed by comparing post synthesis loop latency tables between our design and one with a single unit design. This initial design can help in designing FPGAs targeted for deep learning algorithms that can compete with GPUs in terms of performance.

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Over last several decades, computer vision researchers have been devoted to find good feature to solve different tasks, object recognition, object detection, object segmentation, activity recognition and so forth. Ideal features transform raw pixel intensity values to a representation in which these computer vision problems are easier to solve. Recently, deep feature from covolutional neural network(CNN) have attracted many researchers to solve many problems in computer vision. In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective function for different tasks. More recently, the feature learned from large scale image dataset have been proved to be very effective and generic for many computer vision task. The feature learned from recognition task can be used in the object detection task. This work aims to uncover the principles that lead to these generic feature representations in the transfer learning, which does not need to train the dataset again but transfer the rich feature from CNN learned from ImageNet dataset. This work aims to uncover the principles that lead to these generic feature representations in the transfer learning, which does not need to train the dataset again but transfer the rich feature from CNN learned from ImageNet dataset. We begin by summarize some related prior works, particularly the paper in object recognition, object detection and segmentation. We introduce the deep feature to computer vision task in intelligent transportation system. First, we apply deep feature in object detection task, especially in vehicle detection task. Second, to make fully use of objectness proposals, we apply proposal generator on road marking detection and recognition task. Third, to fully understand the transportation situation, we introduce the deep feature into scene understanding in road. We experiment each task for different public datasets, and prove our framework is robust.

This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face tracking system enables the tracker to run in real-time without any on-line training. This algorithm does not need to change any parameters according to different input video conditions, and the runtime cost will not be affected significantly by an the increase in the number of faces being tracked. In addition, our proposed tracker can overcome most of the generally difficult tracking conditions which include video containing a camera cut, face occlusion, false positive face detection, false negative face detection, e.g. due to faces at the image boundary or faces shown in profile. We use two commonly used metrics to evaluate the performance of our multi-face tracking system demonstrating that our system achieves accurate results. Our multi-face tracker achieves an average runtime cost around 0.035s with GPU acceleration and this runtime cost is close to stable even if the number of tracked faces increases. All the evaluation results and comparisons are tested with four commonly used video data sets.

In recent years, deep convolutional neural networks (CNNs) have shown remarkable results in the image domain. However, most of the neural networks in action recognition do not have very deep layer compared with the CNN in the image domain. This thesis presents a 3D Densely Connected Convolutional Network (3D-DenseNet) for action recognition that can have more than 100 layers without exhibiting performance degradation or overfitting. Our network expands Densely Connected Convolutional Networks (DenseNet) [32] to 3D-DenseNet by adding the temporal dimension to all internal convolution and pooling layers. The internal layers of our model are connected with each other in a feed-forward fashion. In each layer, the feature-maps of all preceding layers are concatenated along the last dimension and are used as inputs to all subsequent layers. We propose two different versions of 3D-DenseNets: general 3D-DenseNet and lite 3D-DenseNet. While general 3D-DenseNet has the same architecture as DenseNet, lite 3D-DenseNet adds a 3D pooling layer right after the first 3D convolution layer of general 3D-DenseNet to reduce the number of training parameters at the beginning so that we can reach a deeper network. We test on two action datasets: the MERL shopping dataset [69] and the KTH dataset [63]. Our experiment results demonstrate that our method performs better than the state-of-the-art action recognition method on the MERL shopping dataset and achieves a competitive result on the KTH dataset.

Demand for localization has been growing due to the increase in location-based services and high bandwidth applications requiring precise localization of users to improve resource management and beam forming. Outdoor localization has been traditionally done through Global Positioning System (GPS), however it’s performance degrades in urban settings due to obstruction and multi-path effects, creating the need for better localization techniques. This thesis proposes a technique using a cascaded approach composed of image classification and deep learning using LIDAR or satellite images and Channel State In-formation (CSI) data from base stations to predict the location of moving vehicles and users outdoors. The algorithm’s performance is assessed using 3 different datasets. The first two use simulated data in the Milli-meter Wave (mmWave) band and lidar images that are collected from the neighbourhood of Rosslyn in Arlington, Virginia. The results show an improvement in localization accuracy as a result of the hierarchical architecture, with a Mean Absolute Error (MAE) of 6.55m for the proposed technique in comparison to a MAE of 9.82m using one Convolutional Neural Network (CNN). The third dataset uses measurements from an LTE mobile communication system along with satellite images that take place at the University of Denmark. The results achieve a MAE of 9.45 m fort he heirchichal approach in comparison to a MAE of 15.74 m for one Feed-Forward Neural Network (FFNN).

In the vast field of signal processing, machine learning is rapidly expanding its domain into all realms. As a constituent of this expansion, this thesis presents contributive work on advancements in machine learning algorithms by building on the shoulder of giants. The first chapter of this thesis contains enhancements to a CNN (convolutional neural network) for better classification of heartbeat arrhythmia. The network goes through a two stage development, the first being augmentations to the network and the second being the implementation of dropout. Chapter 2 involves the combination of CNN and LSTM (long short term memory) networks for the task of short-term energy use data regression. Exploiting the benefits of two of the most powerful neural networks, a unique, novel neural network is created to effectually predict future energy use. The final section concludes this work with directions for future works.

In the field of Natural Language Processing, supervised machine learning is commonly used to solve classification tasks such as sentiment analysis and text categorization. The classical way of representing the text has been to use the well known Bag-Of-Words representation. However lately low-dimensional dense word vectors have come to dominate the input to state-of-the-art models. While few studies have made a fair comparison of the models' sensibility to the text representation, this thesis tries to fill that gap. We especially seek insight in the impact various unsupervised pre-trained vectors have on the performance. In addition, we take a closer look at the Random Indexing representation and try to optimize it jointly with the classification task. The results show that while low-dimensional pre-trained representations often have computational benefits and have also reported state-of-the-art performance, they do not necessarily outperform the classical representations in all cases.

Syfte och frågeställningar: Syftet med denna studie av de globala mediekanalerna CNN och Russia Today är att tydliggöra vilka ståndpunkter som framförs i de undersökta medierna ochatt försöka kartlägga hur Ukrainakonflikten mer övergripande förmedlas. Frågeställningarna är: vilka är skillnaderna och likheterna i kanalernas nyhetsförmedling av händelserna? Vilketbudskap förmedlas av respektive nyhetskanal gällande konflikten? Hur förmedlas budskapen genom ordval? Hur förmedlas budskapen genom källor? Metod och material: Metoden som studien använder sig av är innehållsanalys på ett materialsom är kodad enligt Krippendorff-modellen. Huvudresultat: Skillnader och likheter existerar mellan mediekanalerna CNN och RT i deras nyhetsförmedling och hur budskapen förmedlas. Skillnaderna består i ordval och källor, likheterna består i användningen av källor som uteslutande stödjer påståenden. Budskapensom förmedlas är pro-ukrainskt (CNN) och anti-ukrainskt (RT) och budskapen förmedlas genom laddade ordval och kontextuellt relevanta källor.