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  • 1.
    Deivard, Johannes
    Mälardalen University, School of Innovation, Design and Engineering.
    How accuracy of estimated glottal flow waveforms affects spoofed speech detection performance2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    In the domain of automatic speaker verification,  one of the challenges is to keep the malevolent people out of the system.  One way to do this is to create algorithms that are supposed to detect spoofed speech. There are several types of spoofed speech and several ways to detect them, one of which is to look at the glottal flow waveform  (GFW) of a speech signal. This waveform is often estimated using glottal inverse filtering  (GIF),  since, in order to create the ground truth  GFW, special invasive equipment is required.  To the author’s knowledge, no research has been done where the correlation of GFW accuracy and spoofed speech detection (SSD) performance is investigated. This thesis tries to find out if the aforementioned correlation exists or not.  First, the performance of different GIF methods is evaluated, then simple SSD machine learning (ML) models are trained and evaluated based on their macro average precision. The ML models use different datasets composed of parametrized GFWs estimated with the GIF methods from the previous step. Results from the previous tasks are then combined in order to spot any correlations.  The evaluations of the different methods showed that they created GFWs of varying accuracy.  The different machine learning models also showed varying performance depending on what type of dataset that was being used. However, when combining the results, no obvious correlations between GFW accuracy and SSD performance were detected.  This suggests that the overall accuracy of a GFW is not a substantial factor in the performance of machine learning-based SSD algorithms.

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  • 2.
    Deivard, Johannes
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Johansson, Valentin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Diagnostics Framework for Time-Critical Control Systems in Cloud-Fog Automation2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Evolving technology in wireless telecommunication, such as 5G, provides opportunities to utilize wireless communication more in an industrial setting where reliability and predictability are of great concern. More capable Industrial Internet of Things devices (IIoT) are, indeed, a catalyst for Industry 4.0. Still, before the IIoT devices can be deemed capable enough, a method to evaluate the IIoT systems unobtrusively—so that the evaluation does not affect the performance of the systems—must be established. This thesis aims to answer how the performance of a distributed control system can be unobtrusively evaluated, and also determine what the state-of-the-art is in latency measurements in distributed control systems. To answer the question, a novel diagnostics method for time-critical control systems in cloud-fog automation is proposed and extensively evaluated on real-life testbeds that use 5G, WiFi 6, and Ethernet in an edge-computing topology with real control systems. The feasibility of the proposed method was verified by experiments conducted with a diagnostics framework prototype developed in this thesis. In the proposed diagnostics framework, the controller application is monitored by a computing probe based on an extended Berkeley Packet Filter program. Network communication between the controller and control target is evaluated with a multi-channel Ethernet probe and custom-made software that computes several metrics related to the performance of the distributed system. The data from the unobtrusive probes are sent to a time-series database that is used for further analysis and real-time visualization in a graphical interface created with Grafana. The proposed diagnostics method together with the developed prototype can be used as a research infrastructure for future evaluations of distributed control systems.

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    cfa_diagnostics_framework
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