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Modeling ionospheric variability throughout a proper total electron content (TEC) parameter estimation is a demanding, however, crucial, process for achieving better accuracy and rapid convergence in precise point positioning (PPP). In particular, the single-frequency PPP (SF-PPP) method lacks accuracy due to the difficulty of dealing adequately with the ionospheric error sources. In order to apply ionosphere corrections in techniques, such as SF-PPP, external information of global ionosphere maps (GIMs) is crucial. In this article, we propose a deep learning model to efficiently predict TEC values and to replace the GIM-derived data that inherently have a global character, with equal or better in accuracy regional ones. The proposed model is suitable for predicting the ionosphere delay at different locations of receiver stations. The model is tested during different periods of time, under different solar and geomagnetic conditions and for stations in various latitudes, providing robust estimations of the ionospheric activity at the regional level. Our proposed model is a hybrid model comprising of a 1-D convolutional layer used for the optimal feature extraction and stacked recurrent layers used for temporal time series modeling. Thus, the model achieves good performance in TEC modeling compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting the principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and nonlinear classifiers. The advantages of the proposed classification approach are that: 1) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples); 2) it provides a physical interpretation of model coefficients on the classification output; and 3) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits the principles of logistic regression, assuming the rank-1 canonical decomposition property among its weights. For the nonlinear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state-of-the-art classification methods, either linear (e.g., linear support vector machine) or nonlinear (e.g., deep learning), indicate the outperformance of the proposed scheme, especially in the cases where a small number of training samples is available. [ABSTRACT FROM AUTHOR]

Detection of moving objects in videos is a crucial step toward successful surveillance and monitoring applications. A key component for such tasks is called background subtraction and tries to extract regions of interest from the image background for further processing or action. For this reason, its accuracy and real-time performance are of great significance. Although effective background subtraction methods have been proposed, only a few of them take into consideration the special characteristics of thermal imagery. In this paper, we propose a background subtraction scheme, which models the thermal responses of each pixel as a mixture of Gaussians with unknown number of components. Following a Bayesian approach, our method automatically estimates the mixture structure, while simultaneously it avoids over-/underfitting. The pixel density estimate is followed by an efficient and highly accurate updating mechanism, which permits our system to be automatically adapted to dynamically changing operation conditions. We propose a reference implementation of our method in reconfigurable hardware achieving both adequate performance and low-power consumption. Adopting a high-level synthesis design and demanding floating point arithmetic operations are mapped in reconfigurable hardware, demonstrating fast prototyping and on-field customization at the same time. [ABSTRACT FROM AUTHOR]

The millions of tweets submitted daily overwhelm users who find it difficult to identify content of interest revealing the need for event detection algorithms in Twitter. Such algorithms are proposed in this paper covering both short (identifying what is currently happening) and long term periods (reviewing the most salient recently submitted events). For both scenarios, we propose fuzzy represented and timely evolved tweet-based theoretic information metrics to model Twitter dynamics. The Riemannian distance is also exploited with respect to words’ signatures to minimize temporal effects due to submission delays. Events are detected through a multiassignment graph partitioning algorithm that: 1) optimally retains maximum coherence within a cluster and 2) while allowing a word to belong to several clusters (events). Experimental results on real-life data demonstrate that our approach outperforms other methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the Earliest Deadline First (EDF) and the First Come First Served (FCFS) schemes. Our algorithm uses a max-min fair sharing approach for providing fair access to users. When there is no shortage of resources, the algorithm assigns to each task enough computational power for it to finish within its deadline. When there is congestion, the main idea is to fairly reduce the CPU rates assigned to the tasks so that the share of resources that each user gets is proportional to the user's weight. The weight of a user may be defined as the user's contribution to the infrastructure or the price he is willing to pay for services or any other socioeconomic consideration. In our algorithms, all tasks whose requirements are lower than their fair share CPU rate are served at their demanded CPU rates. However, the CPU rates of tasks whose requirements are larger than their fair share CPU rate are reduced to fit the total available computational capacity in a fair manner. Three different versions of fair scheduling are adopted in this paper: the Simple Fair Task Order (SFTO), which schedules the tasks according to their respective fair completion times, the Adjusted Fair Task Order (AFTO), which refines the SFTO policy by ordering the tasks using the adjusted fair completion time, and the Max-Min Fair Share (MMFS) scheduling policy, which simultaneously addresses the problem of finding a fair task order and assigning a processor to each task based on a max-min fair sharing policy. Experimental results and comparisons with traditional scheduling schemes such as the EDF and the FCFS are presented using three different error criteria. Validation of the simulations using real experiments of tasks generated from 3D image- rendering processes is also provided. The three proposed scheduling schemes can be integrated into existing Grid computing architectures. [ABSTRACT FROM AUTHOR]

In this paper, an interactive framework for navigating video sequences is presented using an optimal content-based video decomposition scheme. In particular, each video sequence is analyzed at different content resolution levels, creating a hierarchy from the lowest (coarse) to the highest (fine) resolution. This content hierarchy is represented as a tree structure, each level of which corresponds to a particular content resolution, while the tree nodes indicate the temporal video segments that the sequence content is partitioned at a given resolution. A criterion is introduced to measure the efficiency of the proposed scheme in organizing the video visual content and to compare it with other hierarchical video content representations and navigation schemes. The efficiency is measured as the difficulty for a user to locate a video segment of interest, while moving through different levels of hierarchy. In our case, video is decomposed so that the best efficiency is accomplished. However, the efficiency of a nonlinear video decomposition scheme depends on: 1) the number of paths required for a user to locate a relevant video segment and 2) the number of shot/frame classes (i.e., content representatives) extracted to represent the visual content. Both issues are addressed in this paper. In the first case, the probability of selecting a relevant video segment in the first path is maximized by extracting optimal content representatives through a minimization of a cross-correlation criterion. For the minimization, a genetic algorithm (GA) is adopted, since application of an exhaustive search to obtain the minimum value is too large to be implemented. The cross-correlation criterion is evaluated on the feature domain by extracting appropriate global and object-based descriptors for each video frame so that a better representation of the visual content is achieved. The second aspect (e.g., the number of content representatives) is addressed by minimizing the average transmitted information and simultaneously taking into consideration the temporal video segment complexity. More content representatives are extracted for video segments of high complexity, whereas a low number is required for low-complexity segments. In addition, a degree of interest is assigned to each video shot (or frame) to address the fact that, from the user's perception, the visual content of a set of shots (frames) satisfies his/her information needs. Finally, a computationally efficient algorithm is proposed to regulate the degree of detail (i.e., the number of shot/frames representatives) in case the visual content is not efficiently represented from the user's perceptive view. Experimental results on real-life video sequences indicate the performance of the proposed GA-based video decomposition scheme compared to other hierarchical video organization methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel relevance feedback algorithm is proposed for improving the performance of inter active content-based retrieval systems. The algorithm recursively estimates the similarity measure, which is used for data ranking in description environments where similarity-based queries are applied, using a set of relevant/irrelevant samples feedback by the user to the system so that the adjusted response is a better approximation of the current user's information needs and preferences. In particulars using concepts of functional analysis, the similarity measure is expressed as a parametric form of known monotone increasing functional components. Then, the contribution of each functional component to the similarity measure is estimated through a recursive and efficient on-line learning algorithm so that: 1) the current user's needs and preferences, as indicated by a set of selected relevant/Irrelevant samples, are satisfied as much as possible, while simultaneously 2) a minimal modification of the already estimated similarity measure is accomplished. Experimental Salts on a large real-life database using objective evaluation criteria, such as the precision-recall curve and the average normalized modified retrieval rank (ANMRR), indicate that the proposed scheme outperforms the compared ones. In addition, the proposed algorithm requires low computational complexity and it can be implemented in a recursive way. [ABSTRACT FROM AUTHOR]

Proposes an unsupervised video object segmentation and tracking algorithm based on an adaptable neural-network architecture. Neural-network retraining strategy; Unsupervised retraining set estimation in videoconference sequences; Specifications of the decision mechanism; Experimental results; Conclusion.

Presents information on a study which described a neural network architecture for recursive nonlinear traffic prediction and modeling of MPEG video sources. Investigation of the problem of online traffic modeling; Implementation of the neural network architecture; Results and discussion; Conclusions.

Deals with a study which proposed an algorithm for non-sequential video content representation. Depiction of the feature-based video representation; Description of the fuzzy classification of feature vectors; Presentation of experimental results using real life video sequences; Concluding remarks.

Features an efficient technique for summarization of stereoscopic video sequences which extracts a small but meaningful set of video frames using a content-based sampling algorithm. Capability of browsing digital stereoscopic video sequences; Efficient content-based queries and indexing; Recursive Shortest Spanning Tree algorithm; Multidimensional fuzzy classification of segment.

Proposes the layer modeling of Motion Picture Experts Group (MPEG)-1 coded video streams and statistical analysis of their characteristics at each layer. Basic characteristics of MPEG-1 encoders; Encoding algorithm; Study of autocorrelation function; Traffic model of frame layer; Intermediate layer modeling; Buffer configuration scheme; Prediction of video activity.

Presents information on a study which described an approach for improving the performance of neural-network classifiers in image recognition, segmentation or coding applications based on a retraining algorithm. Formulation of the problem; Optimal selection of the network retraining data; Decision mechanism for network retraining; Conclusions.

Presents an adaptive algorithm for extracting foreground objects from background in videophone or videoconference applications. Neural network architecture; Incorporation of the algorithm in motion-compensated discrete cosine transform-based coding schemes; Simulation results.

Resource allocation and scheduling are fundamental issues in a Workflow Management System (WfMS). Effective resource management in WfMS should examine resource allocation together with task scheduling since these problems impose mutual constraints. Optimization of the one factor is subject to the other constraints and vice versa. Thus, an ideal algorithm should take into account not only performance metrics of the infrastructure, such as the number of resources and their utilization, but also quality criteria such as the percentage of tasks undergone violation in their temporal restrictions. In this paper, we propose an innovative algorithm which jointly optimizes the two aforementioned contradictory criteria. The algorithm, called Resource Conflicts Joint Optimization (Re.Co.Jo.Op.), minimizes resource conflicts subject to temporal constraints and simultaneously optimizes throughput or utilization subject to resources constraints. To achieve the optimization, the two factors are formulated in a matrix form and the optimal solution is found by applying concepts of the generalized eigenvalue analysis. A rough outline of an agent-based architecture is proposed to achieve runtime integration of our algorithm into a functional WfMS, while experimental results under different load environments and tasks assumption reveal the superiority of the proposed strategy than the other conventional approaches. [ABSTRACT FROM AUTHOR]