In the first part of this dissertation, a novel medium access protocol for the Internet of Thing (IoT) networks is introduced. The Internet of things (IoT), which is the network of physical devices embedded with sensors, actuators, and connectivity, is being accelerated into the mainstream by the emergence of 5G wireless networking. This work presents an uncoordinated non-orthogonal random-access protocol, which is an enhancement to the recently introduced slotted ALOHA- NOMA (SAN) protocol that provides high throughput, while being matched to the low complexity requirements and the sporadic traffic pattern of IoT devices. Under ideal conditions it has been shown that slotted ALOHA-NOMA (SAN), using power- domain orthogonality, can significantly increase the throughput using SIC (Successive Interference Cancellation) to enable correct reception of multiple simultaneous transmitted signals. For this ideal performance, the enhanced SAN receiver adaptively learns the number of active devices (which is not known a priori) using a form of multi-hypothesis testing. For small numbers of simultaneous transmissions, it is shown that there can be substantial throughput gain of 5.5 dB relative to slotted ALOHA (SA) for 0.07 probability of transmission and up to 3 active transmitters. As a further enhancement to SAN protocol, the SAN with beamforming (BF-SAN) protocol was proposed. The BF-SAN protocol uses beamforming to significantly improve the throughput to 1.31 compared with 0.36 in conventional slotted ALOHA when 6 active IoT devices can be successfully separated using 2×2 MIMO and a SIC (Successive Interference Cancellation) receiver with 3 optimum power levels. The simulation results further show that the proposed protocol achieves higher throughput than SAN with a lower average channel access delay. In the second part of this dissertation a novel Machine Learning (ML) approach was applied for proactive mobility management in 5G Virtual Cell (VC) wireless networks. Providing seamless mobility and a uniform user experience, independent of location, is an important challenge for 5G wireless networks. The combination of Coordinated Multipoint (CoMP) networks and Virtual- Cells (VCs) are expected to play an important role in achieving high throughput independent of the mobile’s location by mitigating inter-cell interference and enhancing the cell-edge user throughput. User- specific VCs will distinguish the physical cell from a broader area where the user can roam without the need for handoff, and may communicate with any Base Station (BS) in the VC area. However, this requires rapid decision making for the formation of VCs. In this work, a novel algorithm based on a form of Recurrent Neural Networks (RNNs) called Gated Recurrent Units (GRUs) is used for predicting the triggering condition for forming VCs via enabling Coordinated Multipoint (CoMP) transmission. Simulation results show that based on the sequences of Received Signal Strength (RSS) values of different mobile nodes used for training the RNN, the future RSS values from the closest three BSs can be accurately predicted using GRU, which is then used for making proactive decisions on enabling CoMP transmission and forming VCs. Finally, the work in the last part of this dissertation was directed towards applying Bayesian games for cell selection / user association in 5G Heterogenous networks to achieve the 5G goal of low latency communication. Expanding the cellular ecosystem to support an immense number of connected devices and creating a platform that accommodates a wide range of emerging services of different traffic types and Quality of Service (QoS) metrics are among the 5G’s headline features. One of the key 5G performance metrics is ultra-low latency to enable new delay-sensitive use cases. Some network architectural amendments are proposed to achieve the 5G ultra-low latency objective. With these paradigm shifts in system architecture, it is of cardinal importance to rethink the cell selection / user association process to achieve substantial improvement in system performance over conventional maximum signal-to- interference plus noise ratio (Max-SINR) and Cell Range Expansion (CRE) algorithms employed in Long Term Evolution- Advanced (LTE- Advanced). In this work, a novel Bayesian cell selection / user association algorithm, incorporating the access nodes capabilities and the user equipment (UE) traffic type, is proposed in order to maximize the probability of proper association and consequently enhance the system performance in terms of achieved latency. Simulation results show that Bayesian game approach attains the 5G low end-to-end latency target with a probability exceeding 80%.

This dissertation presents several novel approaches to enhance security, and increase the throughput, and decrease the delay synchronization in 5G networks. First, a new physical layer paradigm was proposed for secure key exchange between the legitimate communication parties in the presence of a passive eavesdropper was presented. The proposed method ensures secrecy via pre-equalization and guarantees reliable communications using Low-Density Parity Check (LDPC) codes. One of the main findings of this research is to demonstrate through simulations that the diversity order of the eavesdropper will be zero unless the main and eavesdropping channels are almost correlated, while the probability of a key mismatch between the legitimate transmitter and receiver will be low. Simulation results demonstrate that the proposed approach achieves very low secret key mismatch between the legitimate users while ensuring very high error probability at the eavesdropper. Next, a novel medium access control (MAC) protocol Slotted Aloha-NOMA (SAN), directed to Machine to Machine (M2M) communication applications in the 5G Internet of Things (IoT) networks was proposed. SAN is matched to the low-complexity implementation and sporadic traffic requirements of M2M applications. Substantial throughput gains are achieved by enhancing Slotted Aloha with non-orthogonal multiple access (NOMA) and a Successive Interference Cancellation (SIC) receiver that can simultaneously detect multiple transmitted signals using power domain multiplexing. The gateway SAN receiver adaptively learns the number of active devices using a form of multi-hypothesis testing and a novel procedure enables the transmitters to independently select distinct power levels. Simulation results show that the throughput of SAN exceeds that of conventional Slotted Aloha by 80% and that of CSMA/CA by 20% with a probability of transmission of 0.03, with a slightly increased average delay owing to the novel power level selection mechanism. Finally, beam sweeping pattern prediction, based on the dynamic distribution of user traffic, using a form of recurrent neural networks (RNNs) called Gated Recurrent Unit (GRU) is proposed. The spatial distribution of users is inferred from data in call detail records (CDRs) of the cellular network. Results show that the user's spatial distribution and their approximate location (direction) can be accurately predicted based on CDRs data using GRU, which is then used to calculate the sweeping pattern in the angular domain during cell search. Furthermore, the data-driven proposed beam sweeping pattern prediction was compared to random starting point sweeping (RSP) to measure the synchronization delay distribution. Results demonstrate the data- drive beam sweeping pattern prediction enables the UE to initially assess the gNB in approximately 0.41 of a complete scanning cycle that is required by the RSP scheme with probability 0.9 in a sparsely distributed UE scenario.

Das mediale patello-femorale Ligament (MPFL) wurde in den letzten Jahren und Jahrzehnten als maßgeblicher passiver Stabilisator der Kniescheibe identifiziert und in biomechanischen Studien beschrieben. Auch wurden die verschiedenen Möglichkeiten der operativen Rekonstruktion des MPFL und deren klinisches Outcome bei patellofemoraler Instabilität in zahlreichen Studien beschrieben sowie relevante Ergebnisse für den klinischen Alltag formuliert. Ziel dieser Arbeit war es, die MPFL-Ersatzplastik als Revisionseingriff mit dem Outcome bei Primäreingriffen zu vergleichen und Faktoren zu bestimmen, die das klinische Outcome beeinflussen. Hierzu wurden 61 Patienten, die von Januar 2009 bis Dezember 2012 in der Klinik für Unfall- und Wiederherstellungschirurgie des Diakoniekrankenhauses Friederikenstift gGmbH Hannover operativ mittels MPFL-Ersatzplastik stabilisiert worden sind, untersucht. Retrospektiv nach Aktenlage und im Rahmen einer klinischen Nachuntersuchung wurden anhand eines standardisierten Studienprotokolls verschiedene Ausgangs- und Outcome-Parameter erhoben und mittels IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp. Released 2011 ausgewertet. Es zeigte sich, dass die Patienten der Primärgruppe eine signifikante Verbesserung durch die Operation erzielen konnten, bei den Patienten der Revisionsgruppe traf dies nicht auf alle Parameter zu. Im Vergleich der Ausgangs- und Outcome-Parameter der Primär- und Revisionsgruppe zeigten sich keine signifikanten Unterschiede. Des Weiteren zeigte sich, dass die Ausgangsparameter Alter bei OP, operativ-versorgte Seite, BMI, Beruf, generelle sportliche Betätigung, verwendete Fadenanker zur Transplantatfixierung an der Patella und Lage der Bohrkanäle in der Patella das Outcome der Operation signifikant beeinflussten.

The research aims to focus on Recurrent Neural Networks (RNN) and its application to the session-aware recommendations empowered by implicit user feedback and content-based metadata. To investigate the promising architecture of RNN, we implement seven different models utilizing various types of implicit feedback and content information. Our results showed that using RNN with complex implicit feedback increases the next-item prediction comparing the baseline models like Cosine Similarity, Doc2Vec, and Item2Vec.

Dyslexia affects between 5-17% of all school children, mak-ing it the most common learning disability. It has beenfound to severely affect learning ability in school subjectsas well as limit the choice of further education and occupa-tion. Since research has shown that early intervention andsupport can mitigate the negative effects of dyslexia, it iscrucial that the diagnosis of dyslexia is easily available andaimed at the right children. To make sure children whoare experiencing problems reading and potentially could bedyslectic are investigated for dyslexia an easy access, sys-tematic, and unbiased screening method would be helpful.This thesis therefore investigates the use of machine learn-ing methods to analyze eye movement patterns for dyslexiaclassification.The results showed that it was possible to separatedyslectic from non-dyslectic readers to 83% accuracy, us-ing non-sequential feature based machine learning methods.Equally good results for lower sample frequencies indicatedthat consumer grade eye trackers can be used for the pur-pose. Furthermore a sequential approach using RecurrentNeural Networks was also investigated, reaching an accu-racy of 78%. The thesis is intended to be an introduction to whatmethods could be viable for identifying dyslexia and as aninspiration for researchers aiming to do larger studies in thearea.

With the rise of social media and publicly available data, opinion mining is more accessible than ever. It is valuable for content creators, companies and advertisers to gain insights into what users think and feel. This work examines comments on YouTube videos, and builds a deep learning classifier to automatically determine their sentiment. Four Long Short-Term Memory-based models are trained and evaluated. Experiments are performed to determine which deep learning model performs with the best accuracy, recall, precision, F1 score and ROC curve on a labelled YouTube Comment dataset. The results indicate that a BiLSTM-based model has the overall best performance, with the accuracy of 89%. Furthermore, the four LSTM-based models are evaluated on an IMDB movie review dataset, achieving an average accuracy of 87%, showing that the models can predict the sentiment of different textual data. Finally, a statistical analysis is performed on the YouTube videos, revealing that videos with positive sentiment have a statistically higher number of upvotes and views. However, the number of downvotes is not significantly higher in videos with negative sentiment.

<div> <div> <div> <p>As the demand and complexity of air traffic increase, it becomes crucial to maintain the safety and efficiency of the operations in airspaces, which, however, could lead to an increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making processes. Although terminal airspaces are highly structured with the flight procedures such as standard terminal arrival routes and standard instrument departures, the aircraft are frequently instructed to deviate from such procedures by ATCs to accommodate given traffic situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to meet scheduling requirements. Such deviation, called vectoring, could even increase the delays and workload of ATCs. This thesis focuses on developing a framework for trajectory pattern identification and classification that can provide ATCs, in vectored airspace, with real-time information of which possible vectoring pattern a new incoming aircraft could take so that such delays and workload could be reduced. This thesis consists of two parts, trajectory pattern identification and trajectory pattern classification. </p> <p>In the first part, a framework for trajectory pattern identification is proposed based on agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean distance as the dissimilarity measure between trajectories. Binary trees with fixes that are provided in the aeronautical information publication data are proposed in order to catego- rize the trajectory patterns. In the second part, multiple recurrent neural network based binary classification models are trained and utilized at the nodes of the binary trees to compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi- cation framework and the classification models are illustrated with the automatic dependent surveillance-broadcast data that were recorded between January and December 2019 in In- cheon international airport, South Korea . </p> </div> </div> </div>

This research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architecture/components of the model, we propose a modified variant of SPSA which in conjunction with back-propagation over time result in a training algorithm with an enhanced convergence for RHN. The trained network is also shown to achieve a better memory recall in the presence of noisy/distorted input. We perform several experiments, using various datasets, to verify the convergence of the training sub-routine, evaluate the impact of different parameters of the model, and compare the performance of the trained RHN in recreating distorted input patterns compared to conventional RBM and Hopfield network and other training methods.

Localization is critical for self-awareness of any autonomous system and is an important part of the autonomous system stack which consists of many phases including sensing, perceiving, planning and control. In the sensing phase, data from on board sensors are collected, preprocessed and passed to the next phase. The perceiving phase is responsible for self awareness or localization and situational awareness which includes multi-objects detection and scene understanding. After the autonomous system is aware of where it is and what is around it, it can use this knowledge to plan for the path it can take and send control commands to pursue this path. In this proposal, we focus on the localization part of the autonomous stack using camera images. We deal with the localization problem from different perspectives including single images and videos. Starting with the single image pose estimation, our approach is to propose systems that not only have good localization accuracy, but also have low space and time complexity. Firstly, we propose SurfCNN, a low cost indoor localization system that uses SURF descriptors instead of the original images to reduce the complexity of training convolutional neural networks (CNN) for indoor localization application. Given a single input image, the strongest SURF features descriptors are used as input to 5 convolutional layers to find its absolute position and orientation in arbitrary reference frame. The proposed system achieves comparable performance to the state of the art using only 300 features without the need for using the full image or complex neural networks architectures. Following, we propose SURF-LSTM, an extension to the idea of using SURF descriptors instead the original images. However, instead of CNN used in SurfCNN, we use long short term memory (LSTM) network which is one type of recurrent neural networks (RNN) to extract the sequential relation between SURF descriptors. Using SURF-LSTM, We only need 50 features to reach comparable or better results compared with SurfCNN that needs 300 features and other works that use full images with large neural networks. In the following research phase, instead of using SURF descriptors as image features to reduce the training complexity, we study the effect of using features extracted from other CNN models that were pretrained on other image tasks like image classification without further training and fine tuning. To learn the pose from pretrained features, graph neural networks (GNN) are adopted to solve the single image localization problem (Pose-GNN) by using these features representations either as features of nodes in a graph (image as a node) or converted into a graph (image as a graph). The proposed models outperform the state of the art methods on indoor localization dataset and have comparable performance for outdoor scenes. In the final stage of single image pose estimation research, we study if we can achieve good localization results without the need for training complex neural network. We propose (Linear-PoseNet) by which we can achieve similar results to the other methods based on neural networks with training a single linear regression layer on image features from pretrained ResNet50 in less than one second on CPU. Moreover, for outdoor scenes, we propose (Dense-PoseNet) that have only 3 fully connected layers trained on few minutes that reach comparable performance to other complex methods. The second localization perspective is to find the relative poses between images in a video instead of absolute poses. We extend the idea used in SurfCNN and SURF-LSTM systems and use SURF descriptors as feature representation of the images in the video. Two systems are proposed to find the relative poses between images in the video using 3D-CNN and 2DCNN-RNN. We show that using 3D-CNN is better than using the combination of CNN-RNN for relative pose estimation. / Graduate

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