Sökning: "Deep Complex Convolutional Neural Network"

Visar resultat 1 - 5 av 44 uppsatser innehållade orden Deep Complex Convolutional Neural Network.

1. 1. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Deepthy Prasad; Swathi Hampapura Sripada; [2023]
   Nyckelord :multivariate - time series; anomaly detection; neural networks; autoencoders; interpretability; counterfactuals;

   Sammanfattning : Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. LÄS MER

3. 2. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

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

   Författare :Javier Ferre Martin; [2023]
   Nyckelord :Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

   Sammanfattning : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. LÄS MER

4. 3. ISAR Imaging Enhancement Without High-Resolution Ground Truth

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Moltas Enåkander; [2023]
   Nyckelord :SAR; SAR Imaging; ISAR; ISAR Imaging; Machine learning; Convolutional neural network; CNN; neural network; Super resolution; Unsupervised learning;

   Sammanfattning : In synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), an imaging radar emits electromagnetic waves of varying frequencies towards a target and the backscattered waves are collected. By either moving the radar antenna or rotating the target and combining the collected waves, a much longer synthetic aperture can be created. LÄS MER

5. 4. 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

7. 5. Rogue Drone Detection

   Master-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Författare :Muiz Olalekan Raheem; [2023]
   Nyckelord :Rogue Drones; Machine Learning; Deep Learning; Radio Frequency; Deep Complex Convolutional Neural Network;

   Sammanfattning : Rogue drones have become a significant concern in recent years due to their potential to cause harm to people and property and disrupt critical infrastructure and public safety. As a result, there has been a growing need for effective methods to detect and mitigate the risks posed by these drones. LÄS MER