459 resources related to Self-organizing networks
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ICC 2021 - IEEE International Conference on Communications
IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
IEEE INFOCOM solicits research papers describing significant and innovative researchcontributions to the field of computer and data communication networks. We invite submissionson a wide range of research topics, spanning both theoretical and systems research.
All fields of satellite, airborne and ground remote sensing.
addresses the discipline of systems engineering,including theory, technology, methodology, andapplications of complex systems, system-of-systems,and integrated systems of national and globalsignificance.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)
IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...
Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...
LTE Advanced: 3GPP Solution for IMT-Advanced, None
IEEE Transactions on Biomedical Engineering, 2000
An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NNs are employed to cluster the data into 25 groups. Using ...
IEEE Transactions on Neural Networks, 2006
In this letter, a preliminary study of habituation in self-organizing networks is reported. The habituation model implemented allows us to obtain a faster learning process and better clustering performances. The habituable neuron is a generalization of the typical neuron and can be used in many self-organizing network models. The habituation mechanism is implemented in a SOM and the clustering performances ...
Proceedings of the 1997 American Control Conference (Cat. No.97CH36041), 1997
Experimental results of controlling the Mach number in a transonic wind tunnel with a system of artificial neural networks are presented. Kohonen self- organizing maps are used to cluster the local tunnel dynamics and thereby predict the Mach number response to candidate control input sequences. The sequence minimizing the predicted error between the desired and actual Mach number is applied ...
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 1992
A multistage network that will reduce the translational uncertainty of a one- dimensional object is presented. To implement this network, novel network structures like multiple-valued outputs, competition between links instead of nodes, and cooperation of signals at the links are used. The number of nodes and links needed to implement the architecture is small. If the input field consists of ...
Robotics History: Narratives and Networks Oral Histories: Chuck Thorpe
Robotics History: Narratives and Networks Oral Histories: Sara Kiesler
Enabling Wireless Autonomous Systems with 5G and Beyond
Robot Ethics in the Era of Self-Driving Automobiles
Organizing Conferences: Engage with the Local Community - Tommy Mayne - Ignite: Sections Congress 2017
Lecture by Dr. Ratnesh Kumar "Vehicle Re-identification for Smart Cities: A New Baseline Using Triplet Embedding"
GHTC 2012 - Robert Freling Keynote
A Conversation About Mind/Brain Research and AI Development: IEEE TechEthics Interview
Brain Fuel: Issues, Opportunities, and Challenges Facing Young Technology Professionals in Industry - 2017
Did You Know: Irwin Jacobs is HKN?!
Welcome Remarks - Ashutosh Dutta - 5G World Forum Santa Clara 2018
Autonomous Driving & Driverless Cars - Grant Imahara and Paul Godsmark from CAVCOE
Norha Villegas: The Role of Models at Runtime in Smart Cyber Physical Systems: WF IoT 2016
Landing in a Self-Flying Airplane. Ready for it? - Antonio Crespo
Who Should the Car Hit? Javier Gozalvez - Ignite: Sections Congress 2017
WIE ILC 2015 - Being Fearless: A Keynote with Patty Hatter of Intel
Q&A with Kathy Grise: IEEE Digital Reality Podcast, Episode 2
802.22: Wireless Regional Area Networks
Shantanu Chakrabartty - SSCS Chip Chat Podcast, Episode 5
An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NNs are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross- correlation clustering method.
In this letter, a preliminary study of habituation in self-organizing networks is reported. The habituation model implemented allows us to obtain a faster learning process and better clustering performances. The habituable neuron is a generalization of the typical neuron and can be used in many self-organizing network models. The habituation mechanism is implemented in a SOM and the clustering performances of the network are compared to the conscience learning mechanism that follows roughly the same principle but is less sophisticated
Experimental results of controlling the Mach number in a transonic wind tunnel with a system of artificial neural networks are presented. Kohonen self- organizing maps are used to cluster the local tunnel dynamics and thereby predict the Mach number response to candidate control input sequences. The sequence minimizing the predicted error between the desired and actual Mach number is applied to the tunnel fan drive system. Comparison is made to gain scheduled automatic control currently in use.
A multistage network that will reduce the translational uncertainty of a one- dimensional object is presented. To implement this network, novel network structures like multiple-valued outputs, competition between links instead of nodes, and cooperation of signals at the links are used. The number of nodes and links needed to implement the architecture is small. If the input field consists of n cells, then the total number of cells needed is only O(n). The total number of connections needed is O(nlogn). It is shown that size- invariant recognition can also be achieved if the input to the architecture is provided by a scale-sensitive network called a masking field.<<ETX>>
Self-Organizing Networks (SONs) aim to raise the level of automated management in cellular technologies. In this field, Load Balancing (LB) and Handover Optimization (HOO) are two important functions to improve network performance. As these two functions can adjust the same parameters, a conflict may happen if LB and HOO tune them at the same time. In this paper, the conflict resolution of both functions in Long-Term Evolution (LTE) networks is addressed. Results show that the proposed coordination effectively provides better performance in those situations in which the adjusted mobility parameter is close to its saturation.
The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior , . DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.
Educational Technology in the last two decades has conquered the position of intermediate between the autonomous fields of pedagogy and technology. This panel contribution aims at outlining the new Ph.D. school in Joensuu. What should we expect from the candidates before they start? What are the unique selling points of the ET Ph.D. School in Joensuu?
The development of mathematical neural networks was based on an analogy with biological neural networks found in nature. Recently there has been a resurgence in research and understanding in self-organizing networks that are based on other metaphors: genetics, immune systems etc. In this paper a new methodology is presented for creating complex adaptive functional networks (CAFN) that are based on the particle swarm social-psychological metaphor. The proposed social programming methodology is based on combining the particle swarm methodology with the group method of data handling and Cartesian programming.
Self-organizing network (SON) technology aims at autonomously deploying, optimizing and repairing radio access networks (RANs). SON algorithms typically use key performance indicators (KPIs) from the RAN. It is shown that in certain cases, it is essential to take into account the impact of the backhaul state in the design of the SON algorithm. We revisit the base station (BS) load definition taking into account the backhaul state. We provide an analytical formula for the load along with a simple estimator for both elastic and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load estimator in a self-optimized load balancing (LB) algorithm. Simulation results for a backhaul constrained heterogeneous network illustrate how the correct load definition can guarantee a proper operation of the SON algorithm.
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