Sökning: "frequency domain representation"

Visar resultat 1 - 5 av 17 uppsatser innehållade orden frequency domain representation.

  1. 1. Data-driven Interpolation Methods Applied to Antenna System Responses : Implementation of and Benchmarking

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Lucas Åkerstedt; [2023]
    Nyckelord :Model Order Reduction; Loewner Framework; State-space Representation; Nevanlinna-Pick Interpolation; Vector Fitting; Cauchy interpolation; Antenna System Responses; Benchmarking; MoM; Data-driven; Interpolation; Modelordningsreducering; Loewner Ramverk; Tillståndsrepresentation; NevanlinnaPick Interpolering; Vector Fitting; Cauchy Interpolering; Antennsystemsvar; Prestandajämförelse; MoM; Datadriven; Interpolering;

    Sammanfattning : With the advances in the telecommunications industry, there is a need to solve the in-band full-duplex (IBFD) problem for antenna systems. One premise for solving the IBFD problem is to have strong isolation between transmitter and receiver antennas in an antenna system. LÄS MER

  3. 2. Real-time unsupervised log event anomaly detection in public transportation

    Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

    Författare :Felicia Segui; Andreas Timürtas; [2022]
    Nyckelord :Technology and Engineering;

    Sammanfattning : Detecting log data anomalies in real-time is useful since it makes it possible to apply logic that corrects the anomalies when they happen. This project presents a method for detecting public transportation bus event log data anomalies in realtime, without having a labeled data set. LÄS MER

  4. 3. Duplicate detection of multimodal and domain-specific trouble reports when having few samples : An evaluation of models using natural language processing, machine learning, and Siamese networks pre-trained on automatically labeled data

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Viktor Karlstrand; [2022]
    Nyckelord :Duplicate detection; Bug reports; Trouble reports; Natural language processing; Information retrieval; Machine learning; Siamese neural network; Transformers; Automated data labeling; Shapley values; Dubblettdetektering; Felrapporter; Buggrapporter; Naturlig språkbehandling; Informationssökning; Maskininlärning; Siamesiska neurala nätverk; Transformatorer; Automatiserad datamärkning; Shapley-värden;

    Sammanfattning : Trouble and bug reports are essential in software maintenance and for identifying faults—a challenging and time-consuming task. In cases when the fault and reports are similar or identical to previous and already resolved ones, the effort can be reduced significantly making the prospect of automatically detecting duplicates very compelling. LÄS MER

  5. 4. Instability of a bi-directional TiFGAN in unsupervised speech representation learning

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Matthaios Stylianidis; [2021]
    Nyckelord :;

    Sammanfattning : A major challenge in the application of machine learning in the speech domain is the unavailability of annotated data. Supervised machine learning techniques are highly dependent on the amount of labelled data and the quality of the labels. LÄS MER

  7. 5. Automated error matching system using machine learning and data clustering : Evaluating unsupervised learning methods for categorizing error types, capturing bugs, and detecting outliers.

    Master-uppsats, Linköpings universitet/Programvara och system

    Författare :Jonatan Bjurenfalk; August Johnson; [2021]
    Nyckelord :Unsupervised learning; machine learning; clustering; DBSCAN; HDBSCAN; X-Means; outlier detection; error log clustering;

    Sammanfattning : For large and complex software systems, it is a time-consuming process to manually inspect error logs produced from the test suites of such systems. Whether it is for identifyingabnormal faults, or finding bugs; it is a process that limits development progress, and requires experience. LÄS MER