Conferences related to Clustering algorithms

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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.


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.


2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Clustering algorithms

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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 ...


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Most published Xplore authors for Clustering algorithms

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Xplore Articles related to Clustering algorithms

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A clustering approach for change detection in SAR images

EUSAR 2012; 9th European Conference on Synthetic Aperture Radar, 2012

This paper presents a new approach for change detection in SAR images based on clustering method. Classic change detection methods use a hard threshold to divide the difference map into two classes: change and unchanged, which has a disadvantage that some weak changed regions are often undetected. Unlike those methods, our proposed method use expectation maximization with graph cut optimization ...


Noise modeling for smoothing the colour histogram

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

In this paper we present a segmentation algorithm for colour images that uses the watershed algorithm to segment either the 2D or the 3D colour histogram of an image. For compliance with the way humans perceive colour, this segmentation has to take place in a perceptually uniform colour space like the Luv space. To avoid oversegmentation, the watershed algorithm has ...


Notice of Violation of IEEE Publication Principles<br>A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining

2006 International Conference on Machine Learning and Cybernetics, 2006

Similarity measure between time series is a key issue in data mining of time series database. Euclidean distance measure is typically used init. However, the measure is an extremely brittle distance measure. Dynamic time warping (DTW) is proposed to deal with this case, but its expensive computation limits its application in massive datasets. In this paper, we present a new ...


Interblock redundancy reduction using quadtrees

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

This paper applies the quadtree structure for image coding. The goal is to adapt the block size and thus to increase the compression ratio (without reducing SNR). Also, the computational time is not significatively increased. It has been applied to Block Truncation Coding of still images, and motion vector coding (interframe). An inter/intraframe application is also discussed.


Font clustering and classification in document images

2000 10th European Signal Processing Conference, 2000

Clustering and identification of fonts in document images impacts on the performance of optical character recognition (OCR). Therefore font features and their clustering tendency are investigated. Font clustering is implemented both from shape similarity and from OCR performance points of view. A font recognition algorithm is developed to identify the font group with which a given text was created.


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Educational Resources on Clustering algorithms

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IEEE-USA E-Books

  • A clustering approach for change detection in SAR images

    This paper presents a new approach for change detection in SAR images based on clustering method. Classic change detection methods use a hard threshold to divide the difference map into two classes: change and unchanged, which has a disadvantage that some weak changed regions are often undetected. Unlike those methods, our proposed method use expectation maximization with graph cut optimization to cluster the difference map into three classes: strong changed areas, weak changed areas and unchanged areas. The experimental results on real SAR images show that our approach obtains a higher detection rate than the previous ones.

  • Noise modeling for smoothing the colour histogram

    In this paper we present a segmentation algorithm for colour images that uses the watershed algorithm to segment either the 2D or the 3D colour histogram of an image. For compliance with the way humans perceive colour, this segmentation has to take place in a perceptually uniform colour space like the Luv space. To avoid oversegmentation, the watershed algorithm has to be applied to a smoothed out histogram. The noise, however, is inhomogeneous in the Luv space and we present here the noise analysis for this space based on assumptions that are experimentally justified.

  • Notice of Violation of IEEE Publication Principles<br>A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining

    Similarity measure between time series is a key issue in data mining of time series database. Euclidean distance measure is typically used init. However, the measure is an extremely brittle distance measure. Dynamic time warping (DTW) is proposed to deal with this case, but its expensive computation limits its application in massive datasets. In this paper, we present a new distance measure algorithm, called local segmented dynamic time warping (LSDTW), which is based on viewing the local DTW measure at the segment level. The DTW measure between the two segments is the product of the square of the distance between their mean times the number of points of the longer segment. Experiments about cluster analysis on the basis of this algorithm were implemented on a synthetic and a real world dataset comparing with Euclidean and classical DTW measure. The experiment results show that the new algorithm gives better computational performance in comparison to classical DTW with no loss of accuracy

  • Interblock redundancy reduction using quadtrees

    This paper applies the quadtree structure for image coding. The goal is to adapt the block size and thus to increase the compression ratio (without reducing SNR). Also, the computational time is not significatively increased. It has been applied to Block Truncation Coding of still images, and motion vector coding (interframe). An inter/intraframe application is also discussed.

  • Font clustering and classification in document images

    Clustering and identification of fonts in document images impacts on the performance of optical character recognition (OCR). Therefore font features and their clustering tendency are investigated. Font clustering is implemented both from shape similarity and from OCR performance points of view. A font recognition algorithm is developed to identify the font group with which a given text was created.

  • Knowledge-independent traffic monitoring: Unsupervised detection of network attacks

    The philosophy of traffic monitoring for detection of network attacks is based on an acquired knowledge perspective: current techniques detect either the well-known attacks on which they are programmed to alert, or those anomalous events that deviate from a known normal operation profile or behavior. In this article we discuss the limitations of current knowledge-based strategy to detect network attacks in an increasingly complex and ever evolving Internet. In a diametrically opposite perspective, we place the emphasis on the development of unsupervised detection methods, capable of detecting network attacks in a changing environment without any previous knowledge of either the characteristics of the attack or the baseline traffic behavior. Based on the observation that a large fraction of network attacks are contained in a small fraction of traffic flows, we demonstrate how to combine simple clustering techniques to accurately identify and characterize malicious flows. To show the feasibility of such a knowledge-independent approach, we develop a robust multiclustering-based detection algorithm, and evaluate its ability to detect and characterize network attacks without any previous knowledge, using packet traces from two real operational networks.

  • WSN Coverage &amp; Connectivity Improvement Utilizing Sensors Mobility

    Mobility in Wireless Sensor Networks may significantly affect the network performance. This paper proposes distributed algorithm that improves the network coverage and connectivity, performing controlled reorganization of mobile sensor nodes (C2 algorithm). The algorithm initially organizes the network in a clustered topology, assuming hexagonal grid structure. The Cluster Heads are positioned in the centres of hexagonal cells designed according the nodes transmission range. The C2 reorganizes the sensor nodes between the adjacent hexagonal cells and equally distributes them within the cells, thus improving the network coverage and providing more uniform energy utilization. The algorithm chooses the optimal nodes to perform the movements, maintaining the connectivity between the sensor nodes and minimizing the energy that the nodes consume for their movement. The simulation results show the benefits of the proposed algorithm implementation for coverage and connectivity improvement in a homogeneous WSN.

  • Steam soft-sensing for dyeing process via FCM-based multiple models

    Aimed to the measuring problem of steam consumption in Dyeing process, a multiple neural network soft sensing modeling of Dyeing steam consumption based on adaptive fuzzy C-means clustering (FCM) is presented. The method is used for separating a whole real-time training data set into several clusters with different centers, and the clustering centers can been modified by an adaptive fuzzy clustering algorithm. Each sub-set is trained by radial base function networks (RBFN), then combining the outputs of sub-models to obtain the finial result. This method has been evaluated by a soft sensing modeling of steam consumption in Dyeing process and a practical case study. The results demonstrate that the method has significant improvement in model prediction accuracy and robustness and a good online measurement capability.

  • Automatic segmentation of facial regions in HSI color format

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Standards related to Clustering algorithms

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