Essays about: "edge devices"

Showing result 6 - 10 of 133 essays containing the words edge devices.

  1. 6. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

    University essay from KTH/Mekatronik och inbyggda styrsystem

    Author : Helgi Hrafn Björnsson; Jón Kaldal; [2023]
    Keywords : Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

    Abstract : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. READ MORE

  3. 7. Dynamic container orchestration for a device-cloud continuum

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Camilo Alfonso Rodriguez Garzon; [2023]
    Keywords : Edge computing; kubernetes operator; dynamic scheduling; Edge computing; Kubernetes-operatör; dynamisk schemaläggning;

    Abstract : Edge computing has emerged as a paradigm to support the growing demand for real-time processing of data generated at the edge of the network. As the devices at the edge are constrained, one of the challenges in the area is how to schedule workloads. READ MORE

  4. 8. Smart Tracking for Edge-assisted Object Detection : Deep Reinforcement Learning for Multi-objective Optimization of Tracking-based Detection Process

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Shihang Zhou; [2023]
    Keywords : Tracking-By-Detection; Deep Reinforcement Learning; Multi-Objective Optimization; Spårning genom detektion; Djup förstärkningsinlärning; Multiobjektiv optimering;

    Abstract : Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. READ MORE

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

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Dario Lo Presti Costantino; [2023]
    Keywords : 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;

    Abstract : 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. READ MORE

  6. 10. Efficient Traffic Monitoring in IoT Networks for Attack Detection at the Edge

    University essay from Uppsala universitet/Institutionen för informationsteknologi

    Author : Engla Jansson; [2023]
    Keywords : ;

    Abstract : The Internet of Things has rapidly expanded over the years, and with this comes significant security risks, with attacks increasing at an alarming rate. A way to detect attacks in the network is by having each device send traffic monitoring information to an edge device that can investigate if any devices have been exploited. READ MORE