Conferences related to Self-organizing feature maps

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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.


2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

The scope of the 2020 IEEE/ASME AIM includes the following topics: Actuators, Automotive Systems, Bioengineering, Data Storage Systems, Electronic Packaging, Fault Diagnosis, Human-Machine Interfaces, Industry Applications, Information Technology, Intelligent Systems, Machine Vision, Manufacturing, Micro-Electro-Mechanical Systems, Micro/Nano Technology, Modeling and Design, System Identification and Adaptive Control, Motion Control, Vibration and Noise Control, Neural and Fuzzy Control, Opto-Electronic Systems, Optomechatronics, Prototyping, Real-Time and Hardware-in-the-Loop Simulation, Robotics, Sensors, System Integration, Transportation Systems, Smart Materials and Structures, Energy Harvesting and other frontier fields.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


2019 IEEE Milan PowerTech

PowerTech is the IEEE PES anchor conference in Europe and has been attended by hundreds of delegates from around the world. It will be an international forum with programme for individuals working in industry and academia, to network, exchange ideas, and discuss the results of their research and development work.

  • 2017 IEEE Manchester PowerTech

    this is IEEE PES anchor conference in Europe covering all areas of electrical power engineering

  • 2015 IEEE Eindhoven PowerTech

    This conference will continue the tradition of the PowerTech conferences held in odd years in Athens, Stockholm, Budapest, Porto, Bologna, St. Petersburg, Lausanne, Bucharest, Trondheim and Grenoble.PowerTech is the anchor conference of the IEEE Power Engineering Society in Europe. It is intended to provide a forum, in the European geographical area, for scientists and engineers interested in electric power engineering to exchange ideas, results of their scientific work, to learn from each other as well as to establish new friendships and rekindle existing ones. Student participation in Power Tech provides an important ingredient toward the event’s success: a special award, the Basil Papadias Award, is presented to the author of the best student paper at each edition. The Power Engineering Society of IEEE organized similar conferences in other parts of the world, such as PowerCon, in the Asia-Pacific region.

  • 2013 IEEE Grenoble PowerTech

    PowerTech is the anchor conference of the IEEE Power & Energy Society in Europe. It is intended to provide a forum for electric power engineering scientists and engineers to share ideas, results of their scientific work, to learn from each other as well as to establish new friendships and maintain existing ones.

  • 2011 IEEE Trondheim PowerTech

    PowerTech is the anchor conference of the IEEE Power & Energy Society in Europe. It is intended to provide a forum for electric power engineering scientists and engineers to share ideas, results of their scientific work and to learn from each other.

  • 2009 IEEE Bucharest Power Tech

    PowerTech is the anchor conference of the IEEE-PES in Europe. It is intended to provide a forum for scientists and engineers interested in electric power engineering to share ideas, results of their scientific work, to learn from each other as well as to establish new friendships and rekindle existing ones.

  • 2007 IEEE Power Tech

  • 2005 IEEE Russia Power Tech

  • 2003 Bologna Power Tech


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Periodicals related to Self-organizing feature maps

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Biomedical Engineering, IEEE Transactions on

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.


Computer Graphics and Applications, IEEE

IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...


Dielectrics and Electrical Insulation, IEEE Transactions on

Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.


Geoscience and Remote Sensing Letters, IEEE

It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...


Geoscience and Remote Sensing, IEEE Transactions on

Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.


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Most published Xplore authors for Self-organizing feature maps

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Xplore Articles related to Self-organizing feature maps

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Clustering ECG complexes using Hermite functions and self-organizing maps

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 ...


Investigation of active channels in multi-channel surface arm EMG recordings for 24 different movements

2014 18th National Biomedical Engineering Meeting, 2014

In this study, a structure based ona Self Organizing Map (SOM) depending on RMS(Root Mean Square), MAV(Mean Absolute Value) and MF(Mean Frequency) features was formed in recording the EMG(Elektromyogram) signals during the performof 24 different movements in hand and fingers to detect of active electrodes.Recorded data with surface EMG electrodes, from 24 channels with 2 kHz sampling frequency as bipolar ...


A comparison between habituation and conscience mechanism in self-organizing maps

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 ...


Neural control of the NASA Langley 16-foot transonic tunnel

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 ...


Mathematical Document Retrieval for Problem Solving

2009 International Conference on Computer Engineering and Technology, 2009

Solving mathematical problems is both challenging and difficult for many students. This paper proposes a document retrieval approach to help solve mathematical problems. The proposed approach is based on Kohonenpsilas Self- Organizing Maps for data clustering of similar mathematical documents from a mathematical document database. Based on a user query problem, similar mathematical documents with their associated solutions are retrieved ...


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Educational Resources on Self-organizing feature maps

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IEEE-USA E-Books

  • Clustering ECG complexes using Hermite functions and self-organizing maps

    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.

  • Investigation of active channels in multi-channel surface arm EMG recordings for 24 different movements

    In this study, a structure based ona Self Organizing Map (SOM) depending on RMS(Root Mean Square), MAV(Mean Absolute Value) and MF(Mean Frequency) features was formed in recording the EMG(Elektromyogram) signals during the performof 24 different movements in hand and fingers to detect of active electrodes.Recorded data with surface EMG electrodes, from 24 channels with 2 kHz sampling frequency as bipolar primarily ispreprocessed. In preprocessing, these data were filtered with 50 Hz notch filter, 3-450 Hz frequency band was selected using the 6th order Butterworth band-pass filter.RMS, MAV and MF features extracting from this EMG data were defined as SOM classifier input. Then, active channels in the classifier output were found for each features and resultswere compared with each other.

  • A comparison between habituation and conscience mechanism in self-organizing maps

    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

  • Neural control of the NASA Langley 16-foot transonic tunnel

    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.

  • Mathematical Document Retrieval for Problem Solving

    Solving mathematical problems is both challenging and difficult for many students. This paper proposes a document retrieval approach to help solve mathematical problems. The proposed approach is based on Kohonenpsilas Self- Organizing Maps for data clustering of similar mathematical documents from a mathematical document database. Based on a user query problem, similar mathematical documents with their associated solutions are retrieved in order to provide hints or solutions on solving the user problem. In this paper, we will discuss the proposed mathematical document retrieval approach. The performance of the proposed approach will also be presented in comparison with other clustering techniques.

  • Self Organizing Maps for Distributed Localization in Wireless Sensor Networks

    Providing an efficient localization system is one of the most important goals to be pursued if an efficient utilization of sensor networks has to be addressed. This paper proposes a novel localization system based on Kohonen 's self organizing maps (SOMs), able to provide some Artificial Intelligence features to sensor nodes. A SOM is a particular neural network that learns to classify data without any supervision. In each sensor node, a SOM is implemented to evaluate the sensor node position, using a very little amount of storage and computing resources. In a scenario where thousands of sensor nodes are placed, this system evaluates the position of each sensor in a distributed manner, assuming a very little percentage of nodes knowing their actual position.

  • Design of a Kinect Sensor Based Posture Recognition System

    The posture recognition system based on Kinect sensor system is developed in this paper. The proposed posture recognition system contains semaphore signals, Kinect 3D sensor system, and self-organizing maps algorithm (SOM). The human-robot interaction is an intuitive and easy operation by posture recognition. However, there are not uniform postures to be a control action criterion. The flag semaphores are adopted in this paper. Then, information of skeleton point is collected by Kinect sensor. The SOM is utilized to analysis the posture information and to recognize the semaphore signal. In experiments, it notes that the posture recognition system can be accomplished to recognize the related semaphore signal.

  • Text independent speaker identification on noisy environments by means of self organizing maps

    We propose an architecture for speaker recognition. This architecture is independent of the text, robust with the presence of noise, and is based on self organizing maps (SOM) (T. Kohonen, 1984). We compare the performance of this architecture for different parametrizations, different signal to noise ratios, with another method for speaker identification based on the arithmetic harmonic spherity measure on covariance matrices (F. Bimbot and L. Mathan, 1993; J. Hernando et al., 1994).

  • Level, downhill and uphill walking identification using neural networks

    Body accelerations during human walking are recorded by a portable measuring device. A new method for parametrising body accelerations is introduced. The parameters are presented to a Kohonen neural network classifier and the feasibility of identification and dissociation of level and walking on a gradient is demonstrated. The most important and original aspect of this classification is its ability to identify the gradient of walking performed in free-living conditions from walking trained on a treadmill.<<ETX>>

  • A Dynamic SOM Algorithm for Clustering Large-Scale Document Collection

    A dynamic SOM algorithm of incremental gradient descent to cluster large-scale document collection is proposed in this paper. In comparison with other SOM algorithms (e.g. GHSOM), the size of output layer in our algorithm can be gradually reduced and dynamically by inserting suitable number of neurons, thus the number of underutilized neurons can be reduced greatly and the training results of this algorithm can fully represent the distribution of topics in document collection. In addition, when using this algorithm to cluster large-scale documents the computation cost can also be shortened remarkably. The overused neurons have been split again to optimize the cluster results further. A good result of cluster can be gained. Experiments results proved the effectiveness of this algorithm.



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