Klaus McDonald-Maier

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Professor of Embedded and Intelligent Systems

University of Essex

United Kingdom

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About

Klaus McDonald-Maier is a Professor of Embedded and Intelligent Systems at the University of Essex. His research interests encompass approximate real-time computing, heterogeneous real-time systems, healthcare data assessment, and anomaly detection frameworks in edge-enabled environments. He has contributed to various recent articles focusing on advanced computing techniques, AI applications in environmental monitoring, and innovative robotic systems.

Recent Grants

Grant: Close

National Centre for Nuclear Robotics (NCNR)

Open Date: 2017-10-01

Close Date: 2021-03-31

Grant: Close

Robust remote sensing for multi-modal characterisation in nuclear and other extreme environments

Open Date: 2017-04-30

Close Date: 2020-04-29

Grant: Close

Security and PrIvacy foR the Internet of Things (SPIRIT)

Open Date: 2017-01-01

Close Date: 2019-12-31

Grant: Close

Human Rights and Information Technology in the Era of Big Data

Open Date: 2015-09-30

Close Date: 2020-09-29

Grant: Close

spEEDO - Energy Efficiency through Debug suppOrt

Open Date: 2013-08-31

Close Date: 2014-05-30

Articles (17)

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2025

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2022

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GERMANY

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Aura Hernandez Sabaté

profesora Agregada

University of Barcelona

SPAIN

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Magnus Själander

Visiting Senior Lecturer

Uppsala University

SWEDEN

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Xiaojun Zhai

University of Essex

UNITED KINGDOM

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Klaus McDonald-Maier

Research Interests

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About

Recent Grants

Articles (17)

PRECIOUS: Approximate Real-Time Computing in MLC-MRAM Based Heterogeneous CMPs

MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

Evaluating the Effect of Surrogate Data Generation on Healthcare Data Assessment

RENOWNED: A Real-Time Anomaly Detection and Mitigation Framework in Edge-Enabled IoV

ARCTIC: Approximate Real-Time Computing in a Cache-Conscious Multicore Environment

Multi-level CEP rules automatic extraction approach for air quality detection and energy conservation decision based on AI technologies

Aggregating Multiple Bio-Inspired Image Region Classifiers for Effective and Lightweight Visual Place Recognition

DELICIOUS: Deadline-Aware Approximate Computing in Cache-Conscious Multicore

Independent and Hybrid Magnetic Manipulation for Full Body Controlled Soft Continuum Robots

Monocular 3D Human Pose Markerless Systems for Gait Assessment

A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment

Weather Classification by Utilizing Synthetic Data

ACCURATE: Accuracy Maximization for Real-Time Multicore Systems With Energy-Efficient Way-Sharing Caches

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

RASA: Reliability-Aware Scheduling Approach for FPGA-Based Resilient Embedded Systems in Extreme Environments

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

An Efficient and Scalable Collection of Fly-Inspired Voting Units for Visual Place Recognition in Changing Environments

Collaborators (8)

Bernard Liew

S. Sanei

Shounak Chakraborty

Pietro Valdastri

Rolf Drechsler

Aura Hernandez Sabaté

Magnus Själander

Xiaojun Zhai

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