Sökning: "Konvolutionella neurala nätverk"

Visar resultat 1 - 5 av 46 uppsatser innehållade orden Konvolutionella neurala nätverk.

1. 1. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

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

   Författare :Xinchen Wang; [2024]
   Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

   Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

3. 2. Heart rate estimation from wrist-PPG signals in activity by deep learning methods

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

   Författare :Marie-Ange Stefanos; [2023]
   Nyckelord :Deep Learning; Medical Data; Signal Processing; Heart Rate Estimation; Wrist Photoplethysmography; Djup lärning; Medicinska Data; Signalbehandling; Pulsuppskattning; Handledsfotopletysmograf;

   Sammanfattning : In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. LÄS MER

4. 3. The impact of pruning Convolutional Neural Networks when classifying skin cancer

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

   Författare :Gustaf Larsson; Marcus Odin; [2023]
   Nyckelord :;

   Sammanfattning : Over the past few years, there have been multiple reports showcasing how Convolutional Neural Networks (CNNs) can be used to classify if skin lesions are cancerous or non-cancerous. However, a limitation of CNNs is the large number of parameters resulting in high computation times. LÄS MER

5. 4. Identifiering av felplacerade komponenter på ett kretskort med bildbehandling

   Uppsats för yrkesexamina på grundnivå, Högskolan i Gävle/Avdelningen för datavetenskap och samhällsbyggnad

   Författare :Adam Chabchoub; [2023]
   Nyckelord :Image processing; Algorithm; Reference image; Histogram matching; Centroid; Bildbehandling; Algoritm; Referensbild; Histogrammatchning; Centroid;

   Sammanfattning : Teknologiska framsteg påverkar tillverkningsindustrin genom att integrera tester för kretskortens status, lödning och elektriska egenskaper. En pick-and-place-maskin används för att verifiera komponentpositioner på kretskortet, men underhåll och re-paration av dessa maskiner medför stora kostnader. Industry 4. LÄS MER

7. 5. Low-power Implementation of Neural Network Extension for RISC-V CPU

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

   Författare :Dario Lo Presti Costantino; [2023]
   Nyckelord :Artificial intelligence; Deep learning; Neural networks; Edge computing; Convolutional neural networks; Low-power electronics; RISC-V; AI accelerators; Parallel processing; Artificiell intelligens; Deep learning; Neurala nätverk; Edge computing; konvolutionella neurala nätverk; Lågeffektelektronik; RISC-V; AI-acceleratorer; Parallell bearbetning;

   Sammanfattning : Deep Learning and Neural Networks have been studied and developed for many years as of today, but there is still a great need of research on this field, because the industry needs are rapidly changing. The new challenge in this field is called edge inference and it is the deployment of Deep Learning on small, simple and cheap devices, such as low-power microcontrollers. LÄS MER