The Experts below are selected from a list of 843 Experts worldwide ranked by ideXlab platform
Chee-kong Chui - One of the best experts on this subject based on the ideXlab platform.
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SMC - Design and implementation of a patient-specific Cognitive Engine for robotic needle insertion
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Chin-boon Chng, Bin Duan, Yvonne Ho, Xuan Chen, Chee-kong ChuiAbstract:In order to develop an effective and user-friendly control method for surgical robotic system, we propose a new framework of Cognitive Engine to supervise and regulate the surgical processes. The framework aims to make the surgical processes understandable by both human operators and robots. A prototype Cognitive Engine was implemented using ontology and SPARQL query language on JAVA and tested in ex-vivo phantom experiments with a robotic RF needle insertion system. The prototype Cognitive Engine has successfully guided the robot in execution of surgical procedures.
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Design and implementation of a patient-specific Cognitive Engine for robotic needle insertion
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Chin-boon Chng, Bin Duan, Yvonne Ho, Xuan Chen, Chee-kong ChuiAbstract:In order to develop an effective and user-friendly control method for surgical robotic system, we propose a new framework of Cognitive Engine to supervise and regulate the surgical processes. The framework aims to make the surgical processes understandable by both human operators and robots. A prototype Cognitive Engine was implemented using ontology and SPARQL query language on JAVA and tested in ex-vivo phantom experiments with a robotic RF needle insertion system. The prototype Cognitive Engine has successfully guided the robot in execution of surgical procedures.
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URAI - Development of a Cognitive Engine for balancing automation and human control of surgical robotic system
2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2014Co-Authors: Chee-kong Chui, Binh P. NguyenAbstract:This paper presents the architecture and work flow of an intelligent surgical robotic system that we are developing. The robotic surgical system which comprises a Cognitive Engine is designed to augment and enhance the hand-eye coordination capability of the surgeon during operation in order to achieve the desired outcome and reduce invasiveness. By incorporating advances in surgical simulation and robot-assisted surgery, patient specific surgical plans can be derived with robot manipulation included. Adaptive visual and haptic cues based are generated based on the complex surgical plan to enhance human control of the robot. We illustrate our approach with an example task analogous to the laparoscopie abdominal surgery, and analyze the user's performance on teleoperation and cooperative control for the example task.
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Development of a Cognitive Engine for balancing automation and human control of surgical robotic system
2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2014Co-Authors: Chee-kong Chui, Binh P. NguyenAbstract:This paper presents the architecture and work flow of an intelligent surgical robotic system that we are developing. The robotic surgical system which comprises a Cognitive Engine is designed to augment and enhance the hand-eye coordination capability of the surgeon during operation in order to achieve the desired outcome and reduce invasiveness. By incorporating advances in surgical simulation and robot-assisted surgery, patient specific surgical plans can be derived with robot manipulation included. Adaptive visual and haptic cues based are generated based on the complex surgical plan to enhance human control of the robot. We illustrate our approach with an example task analogous to the laparoscopie abdominal surgery, and analyze the user's performance on teleoperation and cooperative control for the example task.
Charles W Bostian - One of the best experts on this subject based on the ideXlab platform.
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Application of artificial intelligence to wireless communications
2020Co-Authors: Charles W Bostian, Thomas W. RondeauAbstract:This dissertation provides the theory, design, and implementation of a Cognitive Engine, the enabling technology of Cognitive radio. A Cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The Cognitive Engine, the intelligent system behind the Cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The Cognitive Engine presented here provides a general framework to build and test Cognitive Engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The Cognitive Engine platform allows easy development of new components and algorithms to enhance the Cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system. The dissertation includes discussions of both theory and implementation of the Cognitive Engine. The need for and implementation of all of the Cognitive components is strongly featured as well as the specific issues related to the development of algorithms for Cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-off of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the Cognitive Engine’s capabilities through feed-back, learning, and knowledge representation. The results of this work include the analysis of Cognitive radio design and implementation and the functional Cognitive Engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing Cognitive components to the Cognitive Engine enable the use and extension of the Cognitive Engine for future work.
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Cognitive Engine design
2016Co-Authors: Charles W Bostian, Nicholas J Kaminski, Almohanad S FayezAbstract:A Cognitive Engine (CE) is an intelligent package that turns the knobs and reads the meters of a controllable radio system. As discussed in Chapter 1, this concept was initially introduced as a software entity that interacts with an electronically configurable radio transceiver [1]. Early examples exhibited limited functionality and software portability, often focusing on fairly specific problems or approaches that were very dependent on particular aspects of the support platform. Subsequent work on the topic has developed many of the trends pioneered by these early examples and expanded the domain of a CE. However, it is important to consider that not much more than a decade has passed since the inception of the concept of a CE for radio application; there are a great many challenges left unsolved and capabilities left undiscovered. In fact, the full scope of CE is only beginning to develop and there is a vast territory left to explore.
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DySPAN - CSERE (Cognitive System Enabling Radio Evolution): A modular and user-friendly Cognitive Engine
2012 IEEE International Symposium on Dynamic Spectrum Access Networks, 2012Co-Authors: Alex Young, Almohanad S Fayez, Nicholas J Kaminski, Charles W BostianAbstract:CSERE (Cognitive System Enabling Radio Evolution) is a high performance modular Cognitive Engine written in Python which can control a wide variety of radio platforms to implement fully functional Cognitive radios. Its modular architecture allows CSERE to hot swap software components like objective analyzers (objective function calculators), rankers, and environmental sensors, based on the evolving needs of the Cognitive radio's mission and changes in the RF environment. Using an embedded version of CSERE running on a US $150 BeagleBoard single-board computer and controlling a US $12 Hope RF RFM22B RFIC, the authors have built Cognitive radios small enough to install on Lego robots and inexpensive enough for student laboratory work. The CSERE software is available for research purposes at no cost.
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CSERE (Cognitive System Enabling Radio Evolution): A modular and user-friendly Cognitive Engine
2012 IEEE International Symposium on Dynamic Spectrum Access Networks, 2012Co-Authors: Alexander R. Young, Nicholas J Kaminski, Almohanad Fayez, Charles W BostianAbstract:CSERE (Cognitive System Enabling Radio Evolution) is a high performance modular Cognitive Engine written in Python which can control a wide variety of radio platforms to implement fully functional Cognitive radios. Its modular architecture allows CSERE to hot swap software components like objective analyzers (objective function calculators), rankers, and environmental sensors, based on the evolving needs of the Cognitive radio's mission and changes in the RF environment. Using an embedded version of CSERE running on a US $150 BeagleBoard single-board computer and controlling a US $12 Hope RF RFM22B RFIC, the authors have built Cognitive radios small enough to install on Lego robots and inexpensive enough for student laboratory work. The CSERE software is available for research purposes at no cost.
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biologically inspired Cognitive radio Engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
2004Co-Authors: C.j. Rieser, Charles W BostianAbstract:This research focuses on developing a Cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complementary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a Cognitive radio simulation toolset for evaluating the behavior the Cognitive Engine, and using this toolset to analyze the Cognitive Engine's performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing Cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The Cognitive Engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the Cognitive Engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the Engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the Engine's operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and Engine mathematics to avoid Engine solution errors. Future research also could include extending the Cognitive Engine to a Cognitive radio network and investigating implications for secure communications.
Iztok Humar - One of the best experts on this subject based on the ideXlab platform.
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Data-Driven Resource Management in a 5G Wearable Network Using Network Slicing Technology
IEEE Sensors Journal, 2019Co-Authors: Yixue Hao, Yingying Jiang, Shamim M. Hossain, Ahmed Ghoneim, Jun Yang, Iztok HumarAbstract:The rapid development of the wearable technology brings an explosive growth of wearable devices and imposes a new challenge to the current network. This is because the wearable devices require real-time interaction and data processing. To cope with this challenge and to realize reasonable utilization of resources, this paper first introduces the network slice-based 5G wearable networks, including the 5G ultra-dense cellular network, the edge caching, and the edge computing. Then, in order to realize the service aware and efficient management of network slicing resources, we propose a data-driven resource management framework which includes the service Cognitive Engine, the resources Cognitive Engine, and the global Cognitive Engine. Furthermore, through information perception, analytical prediction, policy decisions, and performance evaluation, the data-driven resources management method is realized. Finally, we set up a real testbed and conduct a related experiment. The experimental results show that the data-driven resources management scheme can realize the service-aware resources allocation and improve the utilization ratio of resources.
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network slicing technology in a 5g wearable network
IEEE Communications Standards Magazine, 2018Co-Authors: Yixue Hao, Daxin Tian, Giancarlo Fortino, Jing Zhang, Iztok HumarAbstract:With the popularization of wearable devices, designing a network architecture that can meet the latency requirements of different wearable devices and can also efficiently utilize network communication, storage, and computation resources will be a challenging problem. In this article, we first propose a new 5G wearable network based on 5G ultra-dense cellular network and mobile edge computing, to meet the access and latency requirements of wearable devices. Then we use network slicing technology in the proposed 5G wearable network to enhance the network resource sharing and energy-efficient utilization. In addition, based on the service Cognitive Engine and network Cognitive Engine deployed in the network, we introduce data-driven network slicing management to adjust the network resources in accordance with the wearable service dynamics. Finally, some challenges and open issues are discussed.
Yixue Hao - One of the best experts on this subject based on the ideXlab platform.
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Data-Driven Resource Management in a 5G Wearable Network Using Network Slicing Technology
IEEE Sensors Journal, 2019Co-Authors: Yixue Hao, Yingying Jiang, Shamim M. Hossain, Ahmed Ghoneim, Jun Yang, Iztok HumarAbstract:The rapid development of the wearable technology brings an explosive growth of wearable devices and imposes a new challenge to the current network. This is because the wearable devices require real-time interaction and data processing. To cope with this challenge and to realize reasonable utilization of resources, this paper first introduces the network slice-based 5G wearable networks, including the 5G ultra-dense cellular network, the edge caching, and the edge computing. Then, in order to realize the service aware and efficient management of network slicing resources, we propose a data-driven resource management framework which includes the service Cognitive Engine, the resources Cognitive Engine, and the global Cognitive Engine. Furthermore, through information perception, analytical prediction, policy decisions, and performance evaluation, the data-driven resources management method is realized. Finally, we set up a real testbed and conduct a related experiment. The experimental results show that the data-driven resources management scheme can realize the service-aware resources allocation and improve the utilization ratio of resources.
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network slicing technology in a 5g wearable network
IEEE Communications Standards Magazine, 2018Co-Authors: Yixue Hao, Daxin Tian, Giancarlo Fortino, Jing Zhang, Iztok HumarAbstract:With the popularization of wearable devices, designing a network architecture that can meet the latency requirements of different wearable devices and can also efficiently utilize network communication, storage, and computation resources will be a challenging problem. In this article, we first propose a new 5G wearable network based on 5G ultra-dense cellular network and mobile edge computing, to meet the access and latency requirements of wearable devices. Then we use network slicing technology in the proposed 5G wearable network to enhance the network resource sharing and energy-efficient utilization. In addition, based on the service Cognitive Engine and network Cognitive Engine deployed in the network, we introduce data-driven network slicing management to adjust the network resources in accordance with the wearable service dynamics. Finally, some challenges and open issues are discussed.
Michael R. Buehrer - One of the best experts on this subject based on the ideXlab platform.
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Robust Training of a link adaptation Cognitive Engine
2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE, 2010Co-Authors: Volos I. Haris, Michael R. BuehrerAbstract:In this paper, we provide a new perspective and insight into the process of finding the maximal performing method using a Cognitive Engine (CE) for link adaptation. It is found that near maximal performance can be reached relatively fast, even when a small number of the available communication methods provide adequate performance. The parameters that affect the expected number of trials are fully discussed along with analytical and simulation results. Finally, we provide the novel Robust Training Algorithm (RoTA), which given at least one method that exceeds the minimum performing requirements, adaptively maintains a communication link with the minimum required performance. The RoTA allows the CE to both continue learning and maintain a stable link for mission-critical applications.
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Cognitive Engine Design for Link Adaptation: An Application to Multi-Antenna Systems
IEEE Transactions on Wireless Communications, 2010Co-Authors: Haris I. Volos, Michael R. BuehrerAbstract:In this paper, we present a Cognitive Engine (CE) design for link adaptation and apply it to a system which can adapt its use of multiple antennas in addition to modulation and coding. Our design moves forward the state of the art in several ways while having a simple structure. Specifically, the CE only needs to observe the number of successes and failures associated with each set of channel conditions and communication method. From these two numbers, the CE can derive all of its functionality. First, it can estimate confidence intervals of the packet success rate (PSR) using the Beta distribution. A low computational approximation to the CDF of the Beta distribution is also presented. Second, the designed CE balances the tradeoff between learning and short-term performance (exploration {vs.} exploitation) by applying the Gittins index. Third, the CE learns the radio abilities independently of the operation objectives. Thus, if an objective changes, information regarding the radio's abilities is not lost. Finally, prior knowledge such as capacity, BER curves, and basic communication principles are used to both initialize the CE's knowledge and maximize the learning rate across different channel conditions. The proposed CE is demonstrated to have the ability to learn in a dynamic scenario and quickly approach maximal performance.
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On Balancing Exploration Vs. Exploitation in a Cognitive Engine for Multi-Antenna Systems
GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009Co-Authors: Haris I. Volos, Michael R. BuehrerAbstract:In this paper, we define the problem of balancing exploration vs. exploitation in a Cognitive Engine controlled multi-antenna communication system in terms of the classical multi-armed bandit framework. We then employ the e-greedy strategy and Gittins' indices methods for addressing the problem in a system with no prior information. Results show that the Gittins' indices assuming a normal reward process had the best overall performance compared to the Gittins' indices with a Bernoulli reward process and the e-greedy strategy. The latter was found to be more consistent albeit inefficient for most of the cases except in the case of both a low number of trials and a low SNR in which it was found to have better performance than the other methods. Nevertheless, the Gittins' indices method should be generally preferred as it is more consistent than the e-greedy strategy across different scenarios.
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Physical layer Cognitive Engine for multi-antenna systems
MILCOM 2008 - 2008 IEEE Military Communications Conference, 2008Co-Authors: Haris I. Volos, Chris I. Phelps, Michael R. BuehrerAbstract:In this paper, we propose a two-step Cognitive Engine that aims to learn its physical layer capabilities in various channel conditions and optimize the delivery of packets in a multi-antenna radio. The first step learns the capabilities of the available techniques, and the second step finds the configuration that best meets the goals of the radio, subject to the channel conditions. This approach allows a good segmentation of the problem and provides the freedom to research each part independently of the other. We propose a reference design based on Bayespsila rule and a brute force search for the learning and optimization stages, respectively. This reference design is to be used as a baseline for the comparison of future implementations. In this paper we also investigate the applicability of the Naive and Semi-Naive Bayespsila models for learning and a binary search for optimization. The Semi-Naive Bayespsila model and a binary search are found to provide good alternatives to the learning and optimization segments of the reference design by requiring the estimation of fewer parameters and fewer searches, respectively. However, the non-reference system sacrifices performance optimality for speed and memory savings as compared to the reference design.