Search for dissertations about: "resource-aware"

Showing result 1 - 5 of 12 swedish dissertations containing the word resource-aware.

2. 1. Resource-aware Wireless Process Control

   Author : Takuya Iwaki; Karl H. Johansson; Sophie Tarbouriech; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Industrial process control; Networked control systems; Wireless network; Event-triggered control; Electrical Engineering; Elektro- och systemteknik;

   Abstract : To tackle the ever-growing demands on high-quality and cost-effective industrial production, recent developments in embedded sensing, wireless communication, and cloud computing offer great opportunities. Resource-aware reliable wireless communication and real-time control are needed to leverage these technologies. READ MORE

4. 2. Design, Implementation and Validation of Resource-Aware and Resilient Wireless Networked Control Systems

   Author : José Araújo; Karl H. Johansson; Jan Lunze; KTH; []
   Keywords : wireless networked control systems; NCS; wireless communications; control; distributed reconfiguration; resilient; fault-tolerant control; IEEE 802.15.4; resource-aware; WNCS; aperiodic control; event-triggered; self-triggered; event-based; co-simulator; estimation; GISOO; MAC; scheduling; routing; RPL; delay; out-of-order communications; CPS; Cyber Physical Systems; Wireless Cyber Physical Systems; Wireless Cyber Physical Control Systems; Electrical Engineering; Elektro- och systemteknik;

   Abstract : Networked control over wireless networks is of growing importance in many application domains such as industrial control, building automation and transportation systems. Wide deployment however, requires systematic design tools to enable efficient resource usage while guaranteeing close-loop control performance. READ MORE

5. 3. A Resource-Aware Framework for Designing Predictable Component-Based Embedded Systems

   Author : Aneta Vulgarakis; Ivica Crnkovic; Paul Pettersson; Cristina Seceleanu; Elisabetta Di Nitto; Mälardalens högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; component-based development; formal analysis; embedded systems; resource prediction; behavioral modeling; architectural modeling; tools; resource-aware framework; Computer Science; datavetenskap;

   Abstract : Managing complexity is an increasing challenge in the development of embedded systems (ES). Some of the factors contributing to the increase in complexity are the growing complexity of hardware and software, and the increased pressure to deliver full-featured products with reduced time-to-market. READ MORE

6. 4. A Resource-Aware Component Model for Embedded Systems

   Author : Aneta Vulgarakis; Ivica Crnkovic; Tiziana Margaria-Steffen; Mälardalens högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; component based software engineering; formal modeling; embedded systems; resources; analysis; behavior; component model; Computer science; Datavetenskap; Computer Science; Datavetenskap;

   Abstract : Embedded systems are microprocessor-based systems that cover a large range of computer systems from ultra small computer-based devices to large systems monitoring and controlling complex processes. The particular constraints that must be met by embedded systems, such as timeliness, resource-use efficiency, short time-to-market and low cost, coupled with the increasing complexity of embedded system software, demand technologies and processes that will tackle these issues. READ MORE

7. 5. Resource-Aware and Personalized Federated Learning via Clustering Analysis

   Author : Ahmed Abbas Mohsin Al-Saedi; Veselka Boeva; Emiliano Casalicchio; György Dán; Blekinge Tekniska Högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Federated Learning; Clustering Analysis; Eccentricity Analysis; Non- IID Data; Model Personalization; Computer Science; Datavetenskap;

   Abstract : Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. READ MORE