{"id":610,"date":"2019-11-19T14:51:50","date_gmt":"2019-11-19T13:51:50","guid":{"rendered":"https:\/\/www.es.mdh.se\/hero\/?page_id=610"},"modified":"2023-10-19T15:58:42","modified_gmt":"2023-10-19T14:58:42","slug":"thesis-topics","status":"publish","type":"page","link":"https:\/\/www.es.mdu.se\/deephero\/thesis-topics\/","title":{"rendered":"Thesis Topics"},"content":{"rendered":"\t\t
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Thesis Topics<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Title:<\/b> Automatic Generation of Network Configuration in Simulated Time Sensitive Networking (TSN) Applications
Subject:<\/b> Computer science, Embedded systems
Level:<\/b> Advanced
Description:<\/b>
IEEE Time-sensitive networking (TSN) standards are the extension of the IEEE Ethernet standards to support high-bandwidth and low-latency real-time communication. TSN standards are promising solutions to be applied in various real-time domains such as automotive, industrial and etc. Designing dependable real-time networks based on TSN standards requires various analysis in terms of behavior, timing and scheduling.
Various simulation tools exist for this purpose. The aim of this project is to automate and enhance simulation tools based on Omnet++ .
The goals of this master thesis are as follows: (a) Study and review the existing simulation tools for TSN based on Omnet++; (b) Investigation and comparison of methods to perform end to end timing analysis of TSN-based ethernet switches using these simulation tools; (c) Investigation on how to automatically configure a simulated TSN network; (d) Investigation on how the automatic network configuration for simulated TSN network can improve the overall performance<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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masoud.daneshtalab@mdu.se<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tTitle:<\/b> Architectural mitigation for systems using Deep Neural Networks (DNNs) in safety-critical applications
\nSubject:<\/b>
\nLevel:<\/b> Advanced
\nDescription:<\/b>
This thesis addresses architectural mitigation techniques for the use of DNNs in safety-critical systems. A DNN has to be trained with data sets of images (or other data) with objects it should be able to classify, but it cannot be trained with all possible inputs. Thus, misclassification of objects may appear. DNNs are also weak to adversarial inputs (the alteration of inputs which forces a trained DNN to misclassify) e.g. due to malicious attacks or external faults caused by the environment such as single event upsets. The focus of this thesis is on redundant architectures that can detect misleading errors. Simulink is the suggested tool for implementing DNNs and associated detection architectures.
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masoud.daneshtalab@mdu.se<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tTitle:<\/b> The implementation of a Recurrent Neural Network Model on Xilinx\u2019s PYNQ board
\nSubject:<\/b> Computer network engineering, Computer science, Embedded systems, Robotics
\nLevel:<\/b> Advanced, Basic
\nDescription:<\/b>
Deep learning has emerged as an important application area for Field-Programmable Gate Arrays (FPGAs). FPGA implementations of machine learning applications can often run much faster than software implementations and can consume significantly less power than Graphics Processing Unit (GPU) implementations. However, these applications mainly focus on large scale FPGA clusters that have an extreme processing power for executing massive matrix or convolution operations but are unsuitable for portable or mobile applications. This thesis will describe research on a single-FPGA platform to explore the applications of FPGAs in these fields.
\nThis thesis will design a Recurrent Neural Network (RNN) for ECG signal classification and implement a hardware accelerator with the AXI Stream interface on a PYNQ board. The PYNQ has a flexible embedded operation system, which makes it suitable to be applied in deep learning applications.
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masoud.daneshtalab@mdu.se<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tTitle:<\/b> Patient-specific method for Classification of ECG signal using Recurrent Neural Networks (RNNs)
\nSubject:<\/b>
\nLevel:<\/b> Advanced, Basic
\nDescription:<\/b>
An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases. By analyzing the electrical signal of each heartbeat, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. This thesis will develop an ECG classification algorithm based on LSTM recurrent neural networks (RNNs).
\nIn this project, a patient-specific procedure will be employed. In other words, the model is trained for every patient individually. Once the model is trained for a patient, continuous ECG monitoring and heartbeat classification is performed in real-time based on the trained model of that patient. Another approach is to train only one model by feeding data from many patients, and then, use the trained model for classification of data from other patients. We do not employ this approach because the ECG waveform varies significantly among different patients.
\nIn the target method, the training data for a patient is formed by combining two sets of data: local ECG data and global ECG data. The first part, i.e., local data, is specific to the patient and helps increase the classification accuracy due to existing similarities among the heartbeats of every patient. The second part, i.e., global data, is the same for all patients. It consists of a number of representative heartbeats from all arrhythmia classes. It helps the model learn other arrhythmia patterns that are not included in the local data.
\nThis thesis will train an LSTM recurrent Neural Network, then test the algorithm on ECG signal data obtained from patients. For this purpose, we aim to process the patient\u2019s ECG signal with python, convert them using Numpy such that it can be fed to the input of LSTM.
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masoud.daneshtalab@mdu.se<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Title:<\/b> Geometry-Aware Synthetic Data Optimization to Achieve better Accuracy vs Robustness Trade-Off
Subject:\u00a0<\/b>
Level:<\/b> Advanced
Description:<\/b>
By adding invisible noise to natural data, adversarial data can readily fool standard-trained deep models, leading to security vulnerabilities in applications such as autonomous driving. A wide range of defence techniques, such as adversarial training (AT), have been proposed to mitigate the adversarial susceptibility of DNNs. However, the AT methods increases the robustness of DNNs, albeit at a significant loss in accuracy and prone to robust over-fitting. Therefore, it has been debated whether robustness and accuracy have a trade-off. Despite these recent advancements, closing the large gap between accuracy and robustness still remains an open challenge. This work aims to take a step towards investigating and improving the trade-off mentioned above and prevent the model to robust over-fitting. To achieve this, we focus on the significance of data in adversarial training. Our hypothesis is that the data points are not equally important in adversarial training. Then, based on the geometry of the DNN\u2019s decision boundary, we proposed a novel method to obtain some valuable synthetic data and incorporate them into adversarial training to enhance the trade-off between accuracy and robustness.<\/p>

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masoud.daneshtalab@mdu.se<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Title:<\/b> Contrastive Self-Supervised Learning in Lane Detection application
Subject:\u00a0<\/b>
Level:<\/b> Advanced
Description:<\/b><\/p>

Summary<\/span>\u00a0<\/span><\/p>

In this thesis, we aim to improve the accuracy of 3D lane detection approaches such as 3DLaneNet<\/span>[1]<\/span> and GenLaneNet<\/span>[2]<\/span> by employing a contrastive self-supervised learning approach.<\/span>\u00a0<\/span><\/p>

Background<\/span>\u00a0<\/span><\/p>

Although Deep supervised learning methods succeed, they are expensive to label data and susceptible to human error. Unsupervised learning approaches are proposed to solve the challenges associated with supervised learning<\/span>[3]<\/span>. Self-supervised learning is one of the newest approaches within the unsupervised learning paradigm. Self-supervised representation learning uses input data as its supervision and is advantageous for nearly all types of downstream tasks<\/span>[4]<\/span>. Contrastive learning is a self-supervised learning technique that aims to keep embedding augmentation versions of the same sample close together while attempting to push embeddings from different samples apart. To increase the accuracy, many researchers<\/span>[1, 2]<\/span> pre-train their proposal networks on a general classification dataset such as ImageNet<\/span>[5]<\/span>. Using pre-trained weight enhances feature extraction performance as a result of training the network with a greater variety of shapes and other visual features. Caron et al. [6] demonstrate that pre-training downstream tasks, such as object detection, through contrastive self-supervised learning requires less labeled data and improves accuracy.<\/span>\u00a0<\/span><\/p>

Goals and Objectives<\/span>\u00a0<\/span><\/p>

We will investigate the following activities in this study:<\/span>\u00a0<\/span><\/p>