Yes / Aggressive posts containing symbolic and offensive images, inappropriate gestures along with provocative textual comments are growing exponentially in social media with the availability of inexpensive data services. These posts have numerous negative impacts on the reader and need an immediate technical solution to filter out aggressive comments. This paper presents a model based on a Convolutional Neural Network (CNN) and Binary Particle Swarm Optimization (BPSO) to classify the social media posts containing images with associated textual comments into non-aggressive, medium-aggressive and high-aggressive classes. A dataset containing symbolic images and the corresponding textual comments was created to validate the proposed model. The framework employs a pre-trained VGG-16 to extract the image features and a three-layered CNN to extract the textual features in parallel. The hybrid feature set obtained by concatenating the image and the text features were optimized using the BPSO algorithm to extract the more relevant features. The proposed model with optimized features and Random Forest classifier achieves a weighted F1-Score of 0.74, an improvement of around 3% over unoptimized features.

Yes / Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.

Yes / Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.

Companies in the transporting sector are undergoing an important transformation of electrifyingtheir fleets to meet the industry’s climate targets. To meet customer’s requests, keep its marketposition, and to contribute to a sustainable transporting industry, Scania needs to be in frontof the evolution. One aspect of this is to attract customers by providing accurate information anddetecting customer’s opportunities for electrification. Understanding the natural behavior of weatherparameters and their impact on energy consumption is crucial for providing accurate simulations ofhow daily operations would appear with an electric truck. The aim of this thesis is to map weatherparameters impact on energy consumption and to get an understanding of the correlations betweenenergy consumption and dynamic weather data. ML and deep learning models have undergone training using historical data from operations per-formed by Scania’s Battery Electric Vehicles(BEV). These models have been assessed against eachother to ensure that they are robust and accurate. Utilizing the trained models ability to providereliable consumption predictions based on weather, we can extract information and patterns aboutconsumption derived from customised weather parameters. The results show several interesting correlations and can quantify the impact of weather parametersunder certain conditions. Temperature is a significant factor that has a negative correlation withenergy consumption while other factors like precipitation and humidity prove less clear results. Byinteracting parameters with each other, some new results were found. For instance, the effect ofhumidity is clarified under certain temperatures. Wind speed also turns out to be an importantfactor with a positive correlation to energy consumption.

Convolutional Neural Networks (CNNs) have traditionally been used for image analysis and computer vision and are known for their ability to detect complex patterns in data. This report studies an application of CNNs within bioelectronic medicine, namely predicting blood glucose levels using nerve signals. Nerve signals and blood glucose levels were measured on a mouse before and after administration of glucose injections. The nerve signals were measured by placing 16 voltage-measuring electrodes on the vagus nerve of the mouse. The obtained nerve signal data was segmented into time intervals of 5 ms and aligned with the corresponding glucose measurements. Two LeNet-5 based CNN architectures, one 1-dimensional and one 2-dimensional, were implemented and trained on the data. Evaluation of the models’ performance was based on the mean squared error, the mean absolute error, and the R2-score of a simple moving average over the dataset. Both models had promising performance with an R2-score of above 0.92, suggesting a strong correlation between nerve signals and blood glucose levels. The difference in performance between the 1-dimensional and 2-dimensional model was insignificant. These results highlight the potential of using CNNs in bioelectronic medicine for prediction of physiological parameters from nerve signal data.

Neural networks are making headlines every day as the tool of the future, powering artificial intelligence programs and supporting technologies never seen before. However, the training of neural networks can take days or even weeks for bigger networks, and requires the use of super computers and GPUs in academia and industry in order to achieve state of the art results. This thesis discusses employing selective measures to determine when to backpropagate and forward propagate in order to reduce training time while maintaining classification performance. This thesis tests these new algorithms on the MNIST and CASIA datasets, and achieves successful results with both algorithms on the two datasets. The selective backpropagation algorithm shows a reduction of up to 93.3% of backpropagations completed, and the selective forward propagation algorithm shows a reduction of up to 72.90% in forward propagations and backpropagations completed compared to baseline runs of always forward propagating and backpropagating. This work also discusses employing the selective backpropagation algorithm on a modified dataset with disproportional under-representation of some classes compared to others. / Master of Science / Neural Networks are some of the most commonly used and best performing tools in machine learning. However, training them to perform well is a tedious task that can take days or even weeks, since bigger networks perform better but take exponentially longer to train. What can be done to reduce training time? Imagine a student studying for a test. The student likely solves practice problems that cover the different topics that may be covered on the test. The student then evaluates which topics he/she knew well, and forgoes extensive practice and review on those in favor of focusing on topics he/she missed or was not as confident on. This thesis discusses following a similar approach in training neural networks in order to reduce their training time needed to achieve desired performance levels.

Specific Emitter Identification (SEI) is the association of a received signal to an emitter, and is made possible by the unique and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency (RF) fingerprint. SEI systems are of vital importance to the military for applications such as early warning systems, emitter tracking, and emitter location. More recently, cognitive radio systems have started making use of SEI systems to enforce Dynamic Spectrum Access (DSA) rules. The use of pre-determined and expert defined signal features to characterize the RF fingerprint of emitters of interest limits current state-of-the-art SEI systems in numerous ways. Recent work in RF Machine Learning (RFML) and Convolutional Neural Networks (CNNs) has shown the capability to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis investigates the ability to use CNNs, in place of a traditional expert-defined feature extraction process, to improve upon traditional SEI systems, by developing and analyzing two distinct approaches for performing SEI using CNNs. Neither approach assumes a priori knowledge of the emitters of interest. Further, both approaches use only raw IQ data as input, and are designed to be easily tuned or modified for new operating environments. Results show CNNs can be used to both estimate expert-defined features and to learn emitter-specific features to effectively identify emitters. / Master of Science / When a device sends a signal, it unintentionally modifies the signal due to small variations and imperfections in the device’s hardware. These modifications, which are typically called the device’s radio frequency (RF) fingerprint, are unique to each device, and, generally, are independent of the data contained within the signal. The goal of a Specific Emitter Identification (SEI) system is to use these RF fingerprints to match received signals to the devices, or emitters, which sent the given signals. SEI systems are often used for military applications, and, more recently, have been used to help make more efficient use of the highly congested RF spectrum. Traditional state-of-the-art SEI systems detect the RF fingerprint embedded in each received signal by extracting one or more features from the signal. These features have been defined by experts in the field, and are determined ahead of time, in order to best capture the RF fingerprints of the emitters the system will likely encounter. However, this use of pre-determined expert features in traditional SEI systems limits the system in a variety of ways. The work presented in this thesis investigates the ability to use Machine Learning (ML) techniques in place of the typically used expert-defined feature extraction processes, in order to improve upon traditional SEI systems. More specifically, in this thesis, two distinct approaches for performing SEI using Convolutional Neural Networks (CNNs) are developed and evaluated. These approaches are designed to have no knowledge of the emitters they may encounter and to be easily modified, unlike traditional SEI systems

This dissertation addresses the challenge of accelerating Convolutional Neural Networks (CNNs) for edge computing in computer vision applications by developing specialized hardware solutions that maintain high accuracy and perform real-time inference. Driven by open-source hardware design frameworks such as FINN and HLS4ML, this research focuses on hardware acceleration, model compression, and efficient implementation of CNN algorithms on AMD SoC-FPGAs using High-Level Synthesis (HLS) to optimize resource utilization and improve the throughput/watt of FPGA-based AI accelerators compared to traditional fixed-logic chips, such as CPUs, GPUs, and other edge accelerators. The dissertation introduces a novel CNN compression technique, "Two-Teachers Net," which utilizes PyTorch FX-graph mode to train an 8-bit quantized student model using knowledge distillation from two teacher models, improving the accuracy of the compressed model by 1%-2% compared to existing solutions for edge platforms. This method can be applied to any CNN model and dataset for image classification and seamlessly integrated into existing AI hardware and software optimization toolchains, including Vitis-AI, OpenVINO, TensorRT, and ONNX, without architectural adjustments. This provides a scalable solution for deploying high-accuracy CNNs on low-power edge devices across various applications, such as autonomous vehicles, surveillance systems, robotics, healthcare, and smart cities.

Die Schätzung von Posen ist ein bedeutendes Forschungsgebiet im Bereich der künstlichen Intelligenz, das die Mensch-Maschine-Interaktion vorantreibt und auch im Sport immer mehr an Relevanz gewinnt. Während menschliche Fußballspieler auf dem Feld mit den Schiedsrichtern ganz natürlich interagieren, wurde dieser Aspekt jedoch bisher in der Standard Platform League des Robocups vernachlässigt. Diese Arbeit untersucht einen weiteren Ansatz, um die Klassifizierung von statischen und dynamischen Schiedsrichterposen durchzuführen und damit dem großen Ziel, dass bis Mitte des 21. Jahrhunderts ein vollständig autonomes Roboter-Team nach den offiziellen FIFA-Regeln gegen den aktuellen Weltmeister gewinnen soll, einen Schritt näher zu kommen. Hierfür wurden Videos von relevanten Schiedsrichterposen erstellt und gesammelt. Anschließend wurden die menschlichen Gelenke mittels MoveNet extrahiert und die Pose mithilfe eines Convolutional Neural Networks klassifiziert. Dabei wurden zwei verschiedene Ansätze verfolgt: Ein Modell für jede Pose und ein Modell für alle Posen. Die Untersuchung zeigt, dass gute bis sehr gute Ergebnisse für statische und dynamische Posen erzielt werden können, wobei die Genauigkeit von einem Modell pro Pose 91,3% bis 99,3% mit einem Durchschnitt von 96,1% erreicht und die Genauigkeit von einem Modell für alle Posen eine Genauigkeit von 90,9% erreicht. Die erfolgreiche Anwendung der entwickelten Methodik zur Schätzung von Posen im Roboterfußball eröffnet vielversprechende Perspektiven für die Zukunft dieses Bereichs. Die gewonnenen Erkenntnisse können nicht nur zur Verbesserung der Leistungsfähigkeit von Fußballrobotern beitragen, sondern auch einen bedeutenden Beitrag zur weiteren Integration von KI-Technologien in unsere Gesellschaft leisten.:Inhaltsverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Abkürzungsverzeichnis 1 Einleitung 2 Einsatzszenario 2.1 Der RoboCup 2.2 Die Standard Platform League 2.3 Die In-Game Visual Referee Challenge 3 Grundlagen neuronaler Netze 3.1 Artificial Neural Networks 3.2 Convolutional Neural Networks 3.2.1 Architektur 3.2.2 Aktivierungsfunktionen 3.2.3 Weitere Optimierungsmöglichkeiten 3.3 Verschiedene Lernmethoden 3.4 Evaluation 4 State of the Art 10 4.1 Machine Learning Ansätze 4.1.1 Decision Trees 4.1.2 k-NN Algorithmus 4.2 Deep Learning Ansätze 4.2.1 Artificial Neural Network 4.2.2 Convolutionan Neural Network 4.2.3 Recurrent Neural Network 4.3 Auswahl des Vorgehens 4.3.1 Schlüsselpunkterkennung 4.3.2 Posenerkennung 5 Eigene Implementierung 5.1 Datensatz 5.2 Vorverarbeitung der Daten 5.2.1 Vorverarbeitung der Videos 5.2.2 Erstellung der Trainings- und Validierungsdaten 5.3 Ansatz 1: Ein Model pro Pose 5.3.1 Datensatz 5.3.2 Architektur 5.3.3 Bewertung 5.4 Ansatz 2: Ein Model für alle Posen 5.4.1 Datensatz 5.4.2 Architektur 5.4.3 Bewertung 5.5 Vergleich der Ansätze 6 Fazit und Ausblick 6.1 Fazit 6.2 Ausblick Literatur A Anhang A.1 RoboCup Standard Platform League (NAO) Technical Challenges A.2 Modelcard Movenet A.3 Code und Datensätze Eigenständigkeitserklärung

Laminar-turbulent transition has a strong impact on aerodynamic performance in many practical applications. Hence, there is a practical need for developing reliable and efficient transition prediction models, which form a critical element of the CFD process for aerospace vehicles across multiple flow regimes. This dissertation explores machine learning approaches to develop transition models using data from computations based on linear stability theory. Such data provide strong correlation with the underlying physics governed by linearized disturbance equations. In the proposed transition model, a convolutional neural network-based model encodes information from boundary layer profiles into integral quantities. Such automated feature extraction capability enables generalization of the proposed model to multiple instability mechanisms, even for those where physically defined shape factor parameters cannot be defined/determined in a consistent manner. Furthermore, sequence-to-sequence mapping is used to predict the transition location based on the mean boundary layer profiles. Such an end-to-end transition model provides a significantly simplified workflow. Although the proposed model has been analyzed for two-dimensional boundary layer flows, the embedded feature extraction capability enables their generalization to other flows as well. Neural network-based nonlinear functional approximation has also been presented in the context of transport equation-based closure models. Such models have been examined for their computational complexity and invariance properties based on the transport equation of a general scalar quantity. The data-driven approaches explored here demonstrate the potential for improved transition prediction models. / Doctor of Philosophy / Surface skin friction and aerodynamic heating caused by the flow over a body significantly increases due to the transition from laminar to turbulent flow. Hence, efficient and reliable prediction of transition onset location is a critical component of simulating fluid flows in engineering applications. Currently available transition prediction tools do not provide a good balance between computational efficiency and accuracy. This dissertation explores machine learning approach to develop efficient and reliable models for predicting transition in a significantly simplified manner. Convolutional neural network is used to extract features from the state of boundary layer flow at each location along the body. These extracted features are then processed sequentially using recurrent neural network to predict the amplification of instabilities in the flow, which is directly correlated to the onset of transition. Such an automated nature of feature extraction enables the generalization of this model to multiple transition mechanisms associated with different flow conditions and geometries. Furthermore, an end-to-end mapping from flow data to transition prediction requires no user expertise in stability theory and provides a significantly simplified workflow as compared to traditional stability-based computations. Another category of neural network-based models (known as neural operators) is also examined which can learn functional mapping from input variable field to output quantities. Such models can learn directly from data for complex set of problems, without the knowledge of underlying governing equations. Such attribute can be leveraged to develop a transition prediction model which can be integrated seamlessly in flow solvers. While further development is needed, such data-driven models demonstrate the potential for improved transition prediction models.