Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review
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[1] Raúl Arrabales Moreno,et al. A Machine Consciousness Approach to Autonomous Mobile Robotics , 2006 .
[2] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.
[3] Todd Litman,et al. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning , 2015 .
[4] Takehisa Yairi,et al. A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.
[5] Jenay M. Beer,et al. Toward a framework for levels of robot autonomy in human-robot interaction , 2014, Journal of human-robot interaction.
[6] Michio Sugeno,et al. Fuzzy Control Systems: Past, Present and Future , 2019, IEEE Computational Intelligence Magazine.
[7] Gordan Jezic,et al. Beyond the Internet of Things: The Social Networking of Machines , 2016, Int. J. Distributed Sens. Networks.
[8] J. Sigl,et al. Anesthesia awareness and the bispectral index. , 2008, The New England journal of medicine.
[9] J. Genova. Turing's sexual guessing game , 1994 .
[10] Luige Vladareanu,et al. Intelligent control interfaces developed on versatile portable intelligent platform in order to improving autonomous navigation robots performances , 2019 .
[11] A. M. Turing,et al. Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.
[12] James S. Albus,et al. Specifying autonomy levels for unmanned systems: interim report , 2004, SPIE Defense + Commercial Sensing.
[13] Varol Akman,et al. Turing Test: 50 Years Later , 2000, Minds and Machines.
[14] Arthur C. Graesser,et al. Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.
[15] John Collier,et al. What is Autonomy , 2002 .
[16] Minoru Asada,et al. Map building for a mobile robot from sensory data , 1990, IEEE Trans. Syst. Man Cybern..
[17] Ray Y. Zhong,et al. Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .
[18] Yu Li,et al. Deep learning in bioinformatics: introduction, application, and perspective in big data era , 2019, bioRxiv.
[19] Gürsel Serpen,et al. AI-WSN: Adaptive and Intelligent Wireless Sensor Network , 2013, Complex Adaptive Systems.
[20] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[21] Ernst D. Dickmanns,et al. Vehicles Capable of Dynamic Vision , 1997, IJCAI.
[22] Keshav Bimbraw,et al. Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology , 2015, 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO).
[23] Luige Vladareanu,et al. The optimization of intelligent control interfaces using Versatile Intelligent Portable Robot Platform , 2016 .
[24] Ahmad Tariq,et al. Intelligent Decision Support Systems- A Framework , 2012 .
[25] Ayoub Ait Lahcen,et al. Big Data technologies: A survey , 2017, J. King Saud Univ. Comput. Inf. Sci..
[26] Ripon Patgiri,et al. Big Data: The V's of the Game Changer Paradigm , 2016, 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS).
[27] Frank L. Lewis,et al. Intelligent Human–Robot Interaction Systems Using Reinforcement Learning and Neural Networks , 2017 .
[28] Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .
[29] Jim Waldo,et al. On system design , 2006, OOPSLA '06.
[30] Jianbin Qiu,et al. Observer-Based Fuzzy Adaptive Event-Triggered Control for Pure-Feedback Nonlinear Systems With Prescribed Performance , 2019, IEEE Transactions on Fuzzy Systems.
[31] Maysam F. Abbod,et al. Applying Time-Frequency Image of Convolutional Neural Network to Extract Feature on Long-Term EEG Signals to Predict Depth of Anesthesia , 2019 .
[32] F. Smarandache,et al. NAO ROBOT IN VIRTUAL ENVIRONMENT APPLIED ON VIPRO PLATFORM , 2022 .
[33] Susumu Tachi,et al. Torque Sensors for Robot Joint Control , 2008 .
[34] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[35] Ramesh C. Jain,et al. Building an environment model using depth information , 1989, Computer.
[36] Panos J. Antsaklis,et al. Control and Machine Intelligence for System Autonomy , 2018, Journal of Intelligent & Robotic Systems.
[37] Hans W. Gottinger,et al. Intelligent decision support systems , 1992, Decis. Support Syst..
[38] Feng Xia,et al. From machine-to-machine communications towards cyber-physical systems , 2013, Comput. Sci. Inf. Syst..
[39] Srinivas Katipamula,et al. Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .
[40] Min Chen,et al. Machine-to-Machine Communications: Architectures, Standards and Applications , 2012, KSII Trans. Internet Inf. Syst..
[41] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[42] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[43] Seref Sagiroglu,et al. Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).
[44] Jelena Kocić,et al. Sensors and Sensor Fusion in Autonomous Vehicles , 2018, 2018 26th Telecommunications Forum (TELFOR).
[45] Nathan D. Gonda,et al. Integrating Virtual and Augmented Reality Based Testing into the Development of Autonomous Vehicles , 2018 .
[46] A. M. Turing,et al. Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.
[47] Qian Liu,et al. Data Fault Detection in Medical Sensor Networks , 2015, Sensors.
[48] R A Brooks,et al. New Approaches to Robotics , 1991, Science.
[49] Meir Kalech,et al. On Fault Detection and Diagnosis in Robotic Systems , 2018, ACM Comput. Surv..
[50] E. Capaldi,et al. The organization of behavior. , 1992, Journal of applied behavior analysis.
[51] Kevin Warwick,et al. Can machines think? A report on Turing test experiments at the Royal Society , 2016, J. Exp. Theor. Artif. Intell..
[52] K.M. Passino,et al. An introduction to autonomous control systems , 1990, IEEE Control Systems.
[53] Fabian Kröger,et al. Automated Driving in Its Social, Historical and Cultural Contexts , 2016 .
[54] K. Passino. Intelligent Control : An Overview of Techniques ∗ , 2000 .
[55] Aleksander Lodwich. Differences between Industrial Models of Autonomy and Systemic Models of Autonomy , 2016, ArXiv.
[56] Tarak Gandhi,et al. Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety , 2007, IEEE Transactions on Intelligent Transportation Systems.
[57] Elena R. Messina,et al. A Framework For Autonomy Levels For Unmanned Systems (ALFUS) , 2005 .
[58] Aleksander Lodwich. How to avoid ethically relevant Machine Consciousness , 2016, ArXiv.
[59] Lyle N. Long,et al. Review of Consciousness and the Possibility of Conscious Robots , 2010, J. Aerosp. Comput. Inf. Commun..
[60] Thomas B. Schön,et al. Using Inertial Sensors for Position and Orientation Estimation , 2017, Found. Trends Signal Process..
[61] Ljubo Vlacic,et al. Sensor fault detection and diagnosis for autonomous vehicles , 2015 .
[62] Oliver Kramer,et al. Genetic Algorithm Essentials , 2017, Studies in Computational Intelligence.
[63] Hubert Eichner,et al. Towards Federated Learning at Scale: System Design , 2019, SysML.
[64] D. A. Linkins. Intelligent Control in Biomedicine , 1994 .
[65] David L. Weissman. Autonomy and Free Will , 2018, Metaphilosophy.
[66] Antonio Fernández-Caballero,et al. Integration of Sensors in Control and Automation Systems , 2017, J. Sensors.
[67] Tom Ziemke,et al. On the role of emotion in biological and robotic autonomy , 2008, Biosyst..
[68] Design & Development of THE ROBUST – An Autonomous Mobile Robot , .
[69] R. French. The Turing Test: the first 50 years , 2000, Trends in Cognitive Sciences.
[70] Gürsel Serpen,et al. Adaptive and intelligent wireless sensor networks through neural networks: an illustration for infrastructure adaptation through Hopfield network , 2016, Applied Intelligence.
[71] Paul M. Thompson,et al. What is where and why it is important , 2007, NeuroImage.