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A Comprehensive Mobility Management Solution for Handling Peak Load in Cellular Network Scenarios

A Comprehensive Mobility Management Solution for Handling Pe...

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6th ACM international workshop on Mobility Management and Wireless Access (MobiWac 2007)

作者： Michaelis, Stefan Lewandowski, Andreas Daniel, Kai Yousaf, Faqir Zarrar Wietfeld, Christian Dortmund Univ Technol CNI Dortmund Germany

ISBN: (纸本)9781605580555

First responders are suffering from insufficient information about the incident during alerting and operations at the scene itself. Particularly at major incident operations detailed and up-to-date multimedia information are indispensable and have to be provided as fast and reliable as possible to the rescue forces. thus, challenging requirements on the mobile communication system like traffic prioritization, seamless handover and everywhere connectivity arise front this use case. In this paper we present a holistic approach for a suitable heterogeneous communication architecture considering the complete mobility management process that consists of mobility modeling, handover trigger algorithms and IP-based handover protocols applied cm a big public event at, a stadium.

关键词： Mobility Model pattern recognition Handover Protocol

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Motion Local Ternary pattern for Distributed Multi-View Human Action recognition

Motion Local Ternary Pattern for Distributed Multi-View Huma...

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6th international Conference on Distributed Smart Cameras (ICDSC)

作者： Luo, Jiajia Qi, Hairong Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781450317726

Multi-view human action recognition has gained a lot of attention in recent years for its superior performance as compared to the single view recognition. In this paper, we propose algorithms for the real-time realization of human action recognition in distributed camera networks (DCNs). We first present a new method for fast calculation of motion information by Motion Local Ternary pattern (Mltp) that is tolerant to illumination change, robust in homogeneous region and computationally efficient. Next, we combine the local interest point detector with Mltp to generate 3D patches containing motion information and introduce two feature descriptors for the extracted 3D patches. Taking advantage of the proposed Mltp, 3D patches generated from background can be further removed automatically and thus the foreground patches can be highlighted. Finally, the histogram representations based on Bag-of-Words modeling, are transmitted from local cameras to the base station for classification. At the base station, a probability model is produced to fuse the information from various views and a class label is assigned accordingly. Compared to the existing algorithms, the proposed methods have three advantages: 1) no preprocessing is required;2) communication among cameras is unnecessary;and 3) positions and orientations of cameras do not need to be fixed. We further evaluate both descriptors on the most popular multi-view action dataset IXMAS. Experimental results indicate that our approaches repeatedly achieve state-of-the-art results when various numbers of views are tested. In addition, our approaches are tolerant to the various combination of views and benefit from introducing more views at the testing stage.

关键词： Cameras

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Robust moment invariant with higher discriminant factor based on Fisher discriminant analysis for symbol recognition

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6th international workshop on graphic recognition

作者： Weliamto, Widya Andyardja Seah, Hock Soon Wibowo, Antonius Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Bandung Inst Technol Dept Elect Engn Bandung 40132 Indonesia

ISBN: (纸本)3540347119

In this paper, we propose a robust moment invariant which has a higher discriminant factor based on Fisher linear discriminant analysis that can deal with noise degradation, deformation of vector distortion, translation, rotation and scale invariant. the proposed system for the symbol recognition consists of 3 steps: 1) degradation model preprocessing step, 2) a different normalization for the second moment invariant and a measure for roundness and eccentricity for feature extraction step, 3) k-Nearest Neighbor with Mahalanobis distance compared to Euclidean distance and k-D tree for classifier. A comparison using multi-layer feed forward neural network classifier is given. An improvement of the discriminant factor around 4 times is achieved compared to that of the original normalized second moments using GREC 2005 dataset. Experimentally we tested our system with 3300 training images using k-NN classifier and on all 9450 images given in the dataset and achieved recognition rates higher than 86 % for all degradation models and 96 % for degradation models 1 to 4.

关键词： pattern recognition

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Evaluating C++ design pattern miner tools

Evaluating C++ design pattern miner tools

引用

6th IEEE international workshop on Source Code Analysis and Manipulation, SCAM 2006

作者： Fülöp, Lajos Jeno Gyovai, Tamás Ferenc, Rudolf University of Szeged Department of Software Engineering Evosoft Hungary Ltd.

ISBN: (纸本)0769523536

Many articles and tools have been proposed over the years for mining design patterns from source code. these tools differ in several aspects, thus their fair comparison is hard. Besides the basic methodology, the main differences are that the tools operate on different representations of the subject system and that the pattern definitions differ as well. In this paper we first provide a common measurement platform for three well-known pattern mining systems, Columbus, Maisa and CrocoPat. then we compare these tools on four C+ + open-source systems: DC+ +, WinMerge, Jikes and Mozilla. Columbus can discover patterns from the C+ + source code itself, while Maisa and CrocoPat require the representation of a software system in a special textual format, so we extended Columbus to provide the common input for the two other tools. We compared these tools in terms of speed, memory consumption and the differences between the hits. While the first two aspects showed comparable results, the recognition capabilities were quite diverse. this is probably due to the significant difference in how the patterns to be recognized and formalized by the tools. therefore we conclude that a more precise and formal description of design patterns would be desirable. © 2006 IEEE.

关键词： Computer software selection and evaluation

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6th European Conference of the international Federation for Medical and Biological Engineering, MBEC 2014

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6th European Conference of the international Federation for Medical and Biological Engineering, MBEC 2014

ISBN: (纸本)9783319111278

the proceedings contain 255 papers. the special focus in this conference is on Biomedical Signal Processing, Biomedical Imaging, Image Processing and Biosensors. the topics include: Acute mental stress assessment via short term HRV analysis in healthy adults;objective estimation of hearing compensation by means of auditory spectral representations;removing electrocardiogram in EMG signals of low intensity;personalized protocol to select usable movements for myoelectric pattern recognition;human identification by simultaneous recording of acceleration and ECG data;multivariate spatial analysis of cognitive signals based on selected intrinsic mode functions;multiple correspondence analysis applied to EEG attributes;Lempel ziv complexity of EEG in depression;Hilbert-Huang time-frequency analysis of motor imagery EEG data for brain computer interfaces;intelligent system for emotion recognition based on electrodermal activity processing;automatic prediction of vascular events by heart rate variability analysis in hypertensive patients;integration of characteristic values with symbolization in knee movement analysis;a multiple treatments meta-analysis of a response to the first-line treatment of the chronic lymphocytic leukemia;denoising pulse wave signal based on wavelet decomposition;uncertainties in measurement of nonlinear dynamics in heart rate variability;a particle filtering method for muscle tissue segmentation from MRI images;blood vessel segmentation and centerline tracking using local structure analysis;preprocess enhancement of CT image for liver segmentation with region growing algorithm and the automated diagnosis of strabismus using an infrared camera.

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On skeletonization in 4D images 6th

引用

6th international workshop on Structural and Syntactical pattern recognition, SSPR 1996

作者： Jonker, Pieter P. Vermeij, Obbe Pattern Recognition Section Delft University of Technology P.O. Box 5046 DelftGA2600 Netherlands

ISBN: (纸本)3540615776

In this paper we extended the 3D skeletonization method based on Euler number count & cluster count to 4D. As the Euler sum in 4D is always 0, we solved this by embedding 4D objects in a 5D space to calculate the 5D Euler number. the 5D Euler number changes due to 3 events: 1D tunnels, 2D tunnels and 3D tunnels. As the latter does not occur in 4D objects, together with a foreground and a background cluster count, the number of equations is equal to the number of unknown variables, and a breakpoint change can be detected. However, this also indicates that making a skeleton in this way is limited to the 4 tb dimension. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Computers

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Automatic bridge crack detection - A texture analysis-based approach 6

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6th IAPR TC3 international workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

作者： Chanda, Sukalpa Bu, Guoping Guan, Hong Jo, Jun Pal, Umapada Loo, Yew-Chaye Blumenstein, Michael School of Information and Communication Technology Griffith University QLD4222 Australia School of Engineering Griffith University QLD4222 Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9783319116556

To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. this article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first stage a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics), and later in the second stage, wavelet features are extracted from the image using a sliding window-based technique. We obtained an overall accuracy of 92.11% while conducting experiments even on noisy and complex bridge images. © Springer international Publishing Switzerland 2014.

关键词： Crack detection

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Identification of structural changes by using statistical pattern recognition 6

Identification of structural changes by using statistical pa...

引用

6th international workshop on Structural Health Monitoring

作者： Gul, M. Catbas, F. N. Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781932078718

Statistical pattern recognition applications have gained considerable attention in to detect structural changes (i.e. damage) that occur over time. Outlier analysis of the features/metrics obtained from the structural health monitoring (SHM) data is used to detect the changes in the structural condition. One of the most challenging aspects of structural condition evaluation of existing constructed facilities by experimental data could be considered as the stochastic nature of the problem. Most of the time, the inherent uncertainties, such as due to environmental effects, can mask the evidence of structural change in the data. Detection of the structural changes is also very dependent on the redundancy of the structure and the level of damage. In this study, a two span 18 ft by 6 ft steel grid is used for different levels of damage on this redundant structure to investigate the effectiveness of statistical methods that have shown promise on other structures. Ambient vibration data is collected. this study accompanies time series modeling (Auto-Regressive model) of response data with a Mahalanobis distance based outlier detection algorithm to detect the structural changes/damage. After explaining the theoretical background of the methodology, example results coming from laboratory tests are presented in a comparative fashion.

关键词： Structural health monitoring

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Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text 6

Depression Assessment by Fusing High and Low Level Features ...

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6th international workshop on Audio-Visual Emotion recognition Challenge - Depression, Mood, and Emotion (AVEC)

作者： Pampouchidou, Anastasia Pediaditis, Matthew Giannakakis, Georgios Marias, Kostas Simantiraki, Olympia Manousos, Dimitrios Meriaudeau, Fabrice Yang, Fan Fazlollahi, Amir Roniotis, Alexandros Simos, Panagiotis Tsiknakis, Manolis Univ Bourgogne Lab Elect Informat & Image Le Creusot France Fdn Res & Technol Hellas FORTH Inst Comp Sci Computat BioMed Lab Iraklion Crete Greece CSIRO Australian E Hlth Res Ctr Brisbane Qld Australia TEIC Biomed Informat & EHlth Lab Iraklion Crete Greece Univ Teknol Petronas CISIR Dept Elect Engn Teronoh Perek Malaysia Univ Crete Sch Med Div Psychiat Iraklion Crete Greece

ISBN: (纸本)9781450345163

Depression is a major cause of disability world-wide. the present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities ( audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. these features were combined with statistically derived features from pre-extracted features ( emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. this approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying notdepressed individuals on the development set and 0.52/0.81, respectively for the test set.

关键词： AVEC 2016 image processing speech processing pattern recognition affective computing depression assessment multimodal fusion

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6th international workshop on Machine Learning in Medical Imaging, MLMI 2015 Held in Conjunction with 18th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

6th International Workshop on Machine Learning in Medical Im...

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6th international workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

ISBN: (纸本)9783319248875

the proceedings contain 40 papers. the special focus in this conference is on Machine Learning in Medical Imaging. the topics include: Segmentation of right ventricle in cardiac MR images using shape regression;visual saliency based active learning for prostate MRI segmentation;a new image data set and benchmark for cervical dysplasia classification evaluation;machine learning on high dimensional shape data from subcortical brain surfaces;node-based Gaussian graphical model for identifying discriminative brain regions from connectivity graphs;functional-anatomical discriminative regions for rest FMRI characterization;craniomaxillofacial deformity correction via sparse representation in coherent space;hep-2 staining pattern recognition using stacked fisher network for encoding Weber local descriptor;supervoxel classification forests for estimating pairwise image correspondences;non-rigid free-form 2d-3d registration using statistical deformation model;learning and combining image similarities for neonatal brain population studies;deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images;predicting standard-dose pet image from low-dose pet and multimodal MR images using mapping-based sparse representation;boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images;brain fiber clustering using non-negative kernelized matching pursuit;detecting abnormal cell division patterns in early stage human embryo development;identification of infants at risk for autism using multi-parameter hierarchical white matter connectomes and multi-atlas context forests for knee MR image segmentation.

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