Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The ...
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Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex imageprocessing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodesfixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and imageprocessing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.
The proceedings contain 30 papers. The special focus in this conference is on Applications of Machine Learning, Cloud Computing, Transportation, Multi-Robot systems and Uncertain systems. The topics include: Maximum l...
ISBN:
(纸本)9783319489438
The proceedings contain 30 papers. The special focus in this conference is on Applications of Machine Learning, Cloud Computing, Transportation, Multi-Robot systems and Uncertain systems. The topics include: Maximum likelihood estimation and optimal coordinates;relation recognition problems and algebraic approach to their solution;prediction of power load demand using modified dynamic weighted majority method;estimating cluster population;evaluation of particle swarm optimisation for medical image segmentation;automated processing of micro-ct scans using descriptor-based registration of 3d images;topic modeling based on frequent sequences graphs;Gaussian process regression with categorical inputs for predicting the blood glucose level;automated information extraction and classification of matrix-based questionnaire data;evaluating raft in docker on kubernetes;performance evaluation of MPTCP transmission of large data objects in computing cloud;a decentralized system for load balancing of containerized microservices in the cloud;layered reconfigurable architecture for autonomous cooperative UAV computing systems;a practical verification of protocol and data format negotiation methods in ComSS platform;reactive dynamic assignment for a bi-dimensional traffic flow model;comparing signal setting design methods through emission and fuel consumption performance indicators;GSOM traffic flow models for networks with information;designing mass-customized service subject to public grid-like network constraints;sensing feedback for the control of multi-joint prosthetic hand;spatio-temporal clustering and forecasting method for free-floating bike sharing systems;comparison of algorithms for constrained multi-robot task allocation and a joint problem of track closure planning and train run rescheduling with detours.
This paper presents a new framework for human action classification using a tensor dynamical model of human action from 3-dimensional (3D) volume sequences and distance measurement on Grassmann manifold. The tensor dy...
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ISBN:
(纸本)9781538607336
This paper presents a new framework for human action classification using a tensor dynamical model of human action from 3-dimensional (3D) volume sequences and distance measurement on Grassmann manifold. The tensor dynamical model is an extension of linear dynamical models for multi-dimensional sequence analysis. Each sub-dimensional linear dynamic model is estimated from tensor sequences using an iterative expectation-maximization (EM) algorithm after projection of tensor sequence to each dimensional axis. The combination of distances on Grassmann manifold of linear dynamic systems in each dimension of the tensor dynamic model provides similarity measurement between two tensor dynamical systems. The proposed approach can be applied to 3D depth or convex hull data as well as 2D video image sequences. Experimental results show good performance in human action recognition from INRIA multiview human action database.
image fusion plays an important role in remote sensing applications. Because of this, the evaluation of the spectral quality of pan-sharpened images is a fundamental subject to optimize and compare the results of diff...
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Smartphones are used by billions of people all over the world and are equipped with various types of sensors and increasingly powerful processors. The number of smartphone-savvy seniors is on the rise due to increased...
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Smartphones are used by billions of people all over the world and are equipped with various types of sensors and increasingly powerful processors. The number of smartphone-savvy seniors is on the rise due to increased efforts to design elderly friendly smartphone systems and applications (apps). These smartphone capabilities, combined with advanced signal processingalgorithms, are a growing platform for different assisted-living solutions, ranging from human-computer interfaces for disabled users to health-monitoring and fitness-tracking devices. This combined software and hardware development, along with the culture of using portable and wearable devices, gives a practical, low-cost, and accessible solution for various assistedliving apps and, in particular, physiological monitoring for home or ambulatory settings [1]. However, many of these solutions are not yet mature enough to be released for public use. Our focus here is health-monitoring apps based on mobile imaging to review the current state of the art and discuss the challenges.
Hyperspectral remote sensing is becoming an active research field in the last decades thanks to the availability of efficient machine learning algorithms and also to the ever-increasing computation power. However, the...
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ISBN:
(纸本)9781509061822
Hyperspectral remote sensing is becoming an active research field in the last decades thanks to the availability of efficient machine learning algorithms and also to the ever-increasing computation power. However, there exist application domains (e.g., embedded applications) in which the deployment of this kind of systems becomes unfeasible due to the high requirements related to the size, power consumption or processing speed. A way to overcome this trouble consists on using any method able to scale-down the dimensionality of the problem and/or to reduce the complexity of the machine learning models. In this paper, we propose the use of a multiobjective genetic algorithm to minimize both the dimension of the input space and the size of the machine learning model. In particular, we have developed a hyperspectral image classifier based on an Extreme Learning Machine (ELM) for which the number of system inputs (dimensionality) and the number of hidden neurons are minimized without decreasing its performance. The system is evaluated by using a known benchmark dataset.
The proceedings contain 105 papers. The special focus in this conference is on Intelligent systems Design and Applications. The topics include: An innovative approach to manage heterogeneous information using relation...
ISBN:
(纸本)9783319534794
The proceedings contain 105 papers. The special focus in this conference is on Intelligent systems Design and Applications. The topics include: An innovative approach to manage heterogeneous information using relational database systems;estimating the number of clusters as a pre-processing step to unsupervised learning;agglomerative and divisive approaches to unsupervised learning in gestalt clusters;improving imputation accuracy in ordinal data using classification;three case studies using agglomerative clustering;a robust and optimally pruned extreme learning machine;investigating the effect of combining text clustering with classification on improving spam email detection;radial basis function neural networks for datasets with missing values;diversification strategies in differential evolution algorithm to solve the protein structure prediction problem;using cluster barycenters for the generalized traveling salesman problem;on pollution attacks in fully connected P2P networks using trusted peers;certification under uncertainties of control methods for multisource elevators;robust and reliable bionic optimization of nonlinear control problems;human detection using biological signals in camera images with privacy aware;nuclei malignancy analysis based on an adaptive bottom-hat filter;test suite prioritization using nature inspired meta-heuristic algorithms;the improvement of an image compression approach using Weber-Fechner law;a minimal rare substructures-based model for graph database indexing;multibiometrics enhancement using quality measurement in score level fusion;effects of random sampling on SVM hyper-parameter tuning and training a spiking neural network to generate online handwriting movements.
These two volumes constitute the Proceedings of the 7th International Workshop on Soft Computing Applications (SOFA 2016), held on 2426 August 2016 in Arad, Romania. This edition was organized by Aurel Vlaicu Universi...
These two volumes constitute the Proceedings of the 7th International Workshop on Soft Computing Applications (SOFA 2016), held on 2426 August 2016 in Arad, Romania. This edition was organized by Aurel Vlaicu University of Arad, Romania, University of Belgrade, Serbia, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics (SRAIT) - Arad Section, General Association of Engineers in Romania - Arad Section, and BTM Resources Arad. The soft computing concept was introduced by Lotfi Zadeh in 1991 and serves to highlight the emergence of computing methodologies in which the accent is on exploiting the tolerance for imprecision and uncertainty to achieve tractability, robustness and lower costs. Soft computing facilitates the combined use of fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing, leading to the concept of hybrid intelligent systems. The rapid emergence of new tools and applications calls for a synergy of scientific and technological disciplines in order to reveal the great potential of soft computing in all domains. The conference papers included in these proceedings, published post-conference, were grouped into the following areas of research: Methods and Applications in Electrical Engineering Knowledge-Based Technologies for Web Applications, Cloud Computing, Security algorithms and Computer Networks Biomedical Applications image, Text and Signal processing Machine Learning and Applications Business Process Management Fuzzy Applications, Theory and Fuzzy Control Computational Intelligence in Education Soft Computing & Fuzzy Logic in Biometrics (SCFLB) Soft Computing algorithms Applied in Economy, Industry and Communication Technology Modelling and Applications in Textiles The book helps to disseminate advances in selected active research directions in the field of soft computing, along with current issu
This paper proposes a new disparity map algorithm which uses block matching and edge-preserving filter. The Sum of Absolute Differences (SAD) algorithm uses block matching technique which produces accurate results on ...
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With the development of science and technology, a variety of office automation systems (OAS) has been extensively applied in various occasions. Moreover, digital imageprocessing technology has made great progress. Th...
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With the development of science and technology, a variety of office automation systems (OAS) has been extensively applied in various occasions. Moreover, digital imageprocessing technology has made great progress. The emergence of a series of excellent algorithms represented by Ada-boost human face detection algorithm extends the application space of digital imageprocessing in daily work and study. Besides, the operational capability of existing personal computers enables them to run smoothly these algorithms, which further contributes to the technological maturity of the digital imageprocessing associated office automation systems. To keep up with the pace of information technology, this study selects high definition (HD) technology for paper archives in OAS, which is related to digital imageprocessing as the research content. Automatic high definition demonstration of paper archives can reduce the burden on staff. This paper solved the problems of correction of slanted document image, automatic extraction of identification photo and color enhancement of seal and verified the feasibility of the scheme.
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