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Visar resultat 1 - 5 av 21 uppsatser som matchar ovanstående sökkriterier.

1. 1. Finding the QRS Complex in a Sampled ECG Signal Using AI Methods

   Master-uppsats, KTH/Fysik

   Författare :Jeanette Marie Victoria Skeppland Hole; [2023]
   Nyckelord :ECG; ECG-analysis; QRS detector; Artificial Intelligence; Machine Learning; Deep neural networks; Long short-term memory; Convolutional neural network; Multilayer perceptron; EKG; EKG-analys; QRS detektor; Artificiell intelligens; Maskininlärning; Djupa neurala nätverk; Long short-term memory; Convolutional neural network; Multilayer perceptron;

   Sammanfattning : This study aimed to explore the application of artificial intelligence (AI) and machine learning (ML) techniques in implementing a QRS detector forambulatory electrocardiography (ECG) monitoring devices. Three ML models, namely long short-term memory (LSTM), convolutional neural network (CNN), and multilayer perceptron (MLP), were compared and evaluated using the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH noise stress test database (NSTDB). LÄS MER

3. 2. LDPC DropConnect

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

   Författare :Xi Chen; [2023]
   Nyckelord :Bayesian approach; Machine learning; Coding theory; Measurement uncertainty; Algorithms; Bayesiansk metod; Maskininlärning; Kodningsteori; Mätosäkerhet; Algoritmer;

   Sammanfattning : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. LÄS MER

4. 3. Straight to the Heart : Classification of Multi-Channel ECG-signals using MiniROCKET

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

   Författare :Stefan Christiansson; [2023]
   Nyckelord :MiniROCKET; Time-series analysis; Multi-variate; Classification; Convolutional Neural Network; MiniROCKET; Tidsserieanalys; Multivariat; Klassifikation; Convolutional Neural Network;

   Sammanfattning : Machine Learning (ML) has revolutionized various domains, with biomedicine standing out as a major beneficiary. In the realm of biomedicine, Convolutional Neural Networks (CNNs) have notably played a pivotal role since their inception, particularly in applications such as time-series classification. LÄS MER

5. 4. Improving deep learning assistedfar-field electromagnetic sidechannelattacks on AES : Effects on attack efficiency from using additive noise and otherdata augmentation techniques

   Master-uppsats, KTH/Mekatronik

   Författare :Axel Zedigh; [2022]
   Nyckelord :;

   Sammanfattning : Profiled side-channel attacks on hardware implemented cryptographic algorithms have been a well-researched topic for the past two decades and many countermeasures against these attacks have been proposed and adopted by the industry. Recently, a new form of far field EM side channel called "screaming channel attacks" have been highlighted. LÄS MER

7. 5. Analyzing the Negative Log-Likelihood Loss in Generative Modeling

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

   Författare :Aleix Espuña I Fontcuberta; [2022]
   Nyckelord :Generative modeling; Normalizing flows; Generative Adversarial Networks; MaximumLikelihood Estimation; Real Non-Volume Preserving flow; Fréchet Inception Distance; Misspecification; Generativa metoder; Normalizing flows; Generative adversarial networks; Maximum likelihood-metoden; Real non-volume preserving flow; Fréchet inception distance; felspecificerade modeller;

   Sammanfattning : Maximum-Likelihood Estimation (MLE) is a classic model-fitting method from probability theory. However, it has been argued repeatedly that MLE is inappropriate for synthesis applications, since its priorities are at odds with important principles of human perception, and that, e.g. LÄS MER