Dr. Fady  Al Najjar
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Dr. Fady Al Najjar

Research Scientist
The Institute of Physical and Chemical Research, Japan


Highest Degree
Ph.D. in System Design Engineering from University of Fukui, Japan

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Biography

Dr. Fady S. Alnajjar is currently working as Research Scientist at Intelligent Behavior Control Unit, Brain Science Institute (BSI), BSI-TOYOTA Collaboration Center (BTCC) of RIKEN (The Institute of Physical and Chemical Research) Nagoya, Japan. He has obtained his PhD in (System Design Engineering) Bio Science and Engineering Lab, Graduate School of Engineering, University of Fukui - Japan. He is member of Japan Neuroscience Society (JNS), IEEE, Computational Intelligence Society (SIC), and IEEE UAE Robotics and Automation Society Chapter. He is also serving as reviewer committee member on International Joint Conference on Neural Networks, Robotics and Autonomous Systems-Journal-Elsevier, Frontier in Neuroscience - Journal volunteer editor in Wikipedia (English/Arabic), Frontiers in ICT and Robotics and AI Springer Open and BioMed Central journal, Frontiers in Psychology, section Cognitive Science, Engineering Application of Artificial Intelligence. His main area of interest focuses on behavior analysis, whether it concerns motor learning and memory (e.g., skill acquisition), or brain’s neural dynamics (e.g., cognition), his aim is to understand the brain mechanisms and the human control system toward developing advanced techniques that enhance the quality of people life (QOL): e.g., constructing an effective neuro-rehabilitation system, designing a smart automobile driving system, making daily activity life more effective and smarter. He has published 48 research articles in journals contributed as author/co-author. He also completed 5 research projects.

Area of Interest:

Computer Sciences
100%
Autonomous Machine
62%
Adaptive Controller
90%
Artificial Cognition
75%
Neural Synergy
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
0
Abstracts
0

Selected Publications

  1. Wojtara, T., F. Alnajjar, S. Shimoda and H. Kimura, 2014. Muscle synergy stability and human balance maintenance. J. Neuroeng. Rehabil., Col. 11. 10.1186/1743-0003-11-129.
    CrossRef  |  Direct Link  |  
  2. Alnajjar, F., M. Itkonen, V. Berenz, M. Tournier, C. Nagai and S. Shimoda, 2014. Sensory synergy as environmental input integration. Front. Neurosci., Vol. 8. 10.3389/fnins.2014.00436.
    CrossRef  |  Direct Link  |  
  3. Alnajjar, F., Y. Yamashita and J. Tani, 2013. The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory. Front. Neurorobot., 7: 1-13.
    Direct Link  |  
  4. Alnajjar, F., T. Wojtara, H. Kimura and S. Shimoda, 2013. Muscle synergy space: Learning model to create an optimal muscle synergy. Front. Comput. Neurosci., Vol. 7. .
    Direct Link  |  
  5. Alnajjar, F., I.M. Zin, A.R. Hafiz and K. Murase, 2013. A tree-type memory formation by sensorimotor feedback: A possible approach to the development of robotic cognition. Intelli. Control Automat., Vol. 4. 10.4236/ica.2013.42020.
    CrossRef  |  Direct Link  |  
  6. Wojtara, T., M. Sasaki, H. Konosu, M. Yamashita, S. Shimoda, F. Alnajjar and H. Kimura, 2012. Artificial balancer-supporting device for postural reflex. Gait Posture, 35: 316-321.
    CrossRef  |  Direct Link  |  
  7. Hafiz, A.R., F. Alnajjar and K. Murase, 2011. A novel bioinspired vision system: A step toward real-time human-robot interactions. J. Robotics, Vol. 2011. 10.1155/2011/943137.
    CrossRef  |  Direct Link  |  
  8. Zin, I.B.M., F. Alnajjar and K. Murase, 2009. A new mechanism to adapt a real mobile robot into a complex environment using Pattern Association Network Controller (PAN-C). J. Adv. Comput. Intelli. Intelligent Informat., 13: 312-319.
  9. Alnajjar, F., I.B.M. Zin and K. Murase, 2009. A hierarchical autonomous robot controller for learning and memory: adaptation in a dynamic environment. Adapt. Behav., 17: 179-196.
    CrossRef  |  
  10. Alnajjar, F. and K. Murase, 2008. A simple aplysia-like spiking neural network to generate adaptive behavior in autonomous robots. Adapt. Behav., 16: 306-324.
    CrossRef  |  Direct Link  |  
  11. Alnajjar, F. and K. Murase, 2006. Self-organization of spiking neural network that generates autonomous behavior in a real mobile robot. Int. J. Neural Syst., 16: 229-239.