Assoc. Prof. Dr. Ioana-Raluca Adochiei | Real-Time | Research Excellence Award

Assoc. Prof. Dr. Ioana-Raluca Adochiei | Real-Time | Research Excellence Award

Assoc. Prof. Dr. Ioana-Raluca Adochiei | Real-Time | Military Technical Academy Ferdinand I | Romania

Assoc. Prof. Dr. Ioana-Raluca Adochiei is an accomplished Romanian academic and researcher based in Bucharest, Romania, with extensive expertise in aerospace engineering, navigation systems, and military technologies, currently serving as Associate Professor, PhD Engineer in the Department of Aircraft Integrated Systems and Mechanics within the Faculty of Aircraft and Military Vehicles at the Military Technical Academy “Ferdinand I”, a position reflecting long-standing academic leadership and dedication to research and education. Assoc. Prof. Dr. Ioana-Raluca Adochiei completed doctoral-level training in engineering with advanced research specialization developed through international academic exposure, including European research internships and Erasmus teaching mobility at Jena University of Applied Sciences in Germany, where strong foundations in signal processing, micro- and nano-sensors, inertial navigation, and intelligent control systems were established and refined. Assoc. Prof. Dr. Ioana-Raluca Adochiei has held key roles as Project Director and researcher engineer in multiple nationally funded strategic research initiatives coordinated by leading Romanian defense and aerospace institutions, including the Academy of Scientists of Romania and the Military Technical Academy “Ferdinand I”, while also representing Romania as a responsible expert in NATO-aligned European Defence Agency working groups focused on Guidance, Navigation, and Control; professional activities span project leadership, systems engineering, management team participation, and interdisciplinary collaboration in security, aerospace observation, GNSS-denied navigation, UAS-based public health support, optical sensor stabilization platforms, and emergency medical technology development.

Citation Metrics (Google Scholar)

900

700

500

300

0

Citations
811

h-index
16

i10-index
21

Citations
h-index
i10-index


View Google Scholar Profile

Featured Publications


Cardiovascular and Cardiorespiratory Coupling Analyses: A Review


– Phil. Trans. Royal Society A, 2013 · 219 citations


Electronic System for Real-Time Indoor Air Quality Monitoring


– E-Health and Bioengineering Conference, 2020 · 36 citations


A New Normalised Short-Time PDC for Dynamic Coupling Analyses


– Biomedical Engineering, 2013 · 28 citations


Concepts for Error Modeling of Miniature Accelerometers Used in Inertial Navigation Systems


– Annals of the University of Craiova, 2010 · 26 citations

Kim Bjerge | Signal Processing | Best Researcher Award

Kim Bjerge | Signal Processing | Best Researcher Award

Mr. Kim Bjerge, Aarhus University, Denmark.

Kim Bjerge is an Associate Professor at Aarhus University in the Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. With a Ph.D. focused on Computer Vision and Deep Learning for Insect Monitoring, Kim combines academic expertise with significant industry experience. He has held various teaching and leadership positions at Aarhus University and has contributed to research projects in computer vision. His work has resulted in a notable H-index of 14 and 1080 citations on Google Scholar. Kim is dedicated to advancing technology in engineering education and research. 🎓💻📈

Publication Profiles 

Googlescholoar

Education and Experience

  • Ph.D. in Computer Vision and Deep Learning for Insect Monitoring (Aarhus University, 2022 – present) 📚
  • M.Sc. Eng. in Information Technology (Aarhus University, 2013) 📖
  • B. Eng. in Electronics Engineering (Engineering College of Aarhus, 1989) 🔧
  • Associate Professor and Group Leader (Aarhus University, 2021 – present) 🎓
  • Associate Professor and Group Leader, Signal Processing (Aarhus University, 2009 – 2020) 📊
  • Senior Consultant, IT-Development (Danish Technological Institute, 2007 – 2009) 🛠️
  • Software Development Manager (TC Electronic A/S, 1999 – 2007) 🎶
  • System Developer (Crisplant A/S, 1996 – 1999) 📦
  • System Manager (Sam-system A/S, 1989 – 1996) 💼

Suitability For The Award

Mr. Kim Bjerge, Associate Professor at Aarhus University’s Department of Electrical and Computer Engineering, is an exemplary candidate for the Best Researcher Award due to his outstanding contributions to computer vision, deep learning, and signal processing. With a remarkable career spanning academia and industry, he has made groundbreaking advancements in the fields of artificial intelligence, embedded systems, and digital signal processing, impacting both research and application development globally.

Professional Development

Kim Bjerge has pursued extensive professional development through various programs. He completed the Pedagogical Programme in Engineering at the Center for Engineering Education Research and Development, earning 10 ECTS credits. Additionally, he participated in project management training at Provinu and various management courses at Aarhus Business College, enhancing his skills in human resources, organizational strategy, and software engineering. His commitment to ongoing learning ensures that he remains at the forefront of engineering education and technology. 📚🔧🌱

Research Focus

Kim Bjerge’s research focuses on the intersection of computer vision, deep learning, and machine learning, particularly in the context of insect monitoring. His work aims to develop innovative solutions that enhance the understanding and management of ecological systems through advanced image analysis and artificial intelligence techniques. By leveraging his expertise in signal processing, he contributes to the development of cutting-edge technologies that have practical applications in various fields, including agriculture and environmental science. 🌱🔍🤖

Publication Top Notes 

  • Deep learning and computer vision will transform entomology – Cited by: 362, Year: 2021 📖
  • Towards the fully automated monitoring of ecological communities – Cited by: 141, Year: 2022 🌱
  • An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning – Cited by: 119, Year: 2021 🦋
  • Real-time insect tracking and monitoring with computer vision and deep learning – Cited by: 110, Year: 2021 📹
  • A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony – Cited by: 85, Year: 2019 🐝
  • Accurate detection and identification of insects from camera trap images with deep learning – Cited by: 61, Year: 2023 🔍
  • A living laboratory exploring mobile support for everyday life with diabetes – Cited by: 40, Year: 2010 📱
  • Hierarchical classification of insects with multitask learning and anomaly detection – Cited by: 26, Year: 2023 📊
  • Enhancing non-technical skills by a multidisciplinary engineering summer school – Cited by: 19, Year: 2017 🎓