Analytical framework for dimensioning hierarchical WiMax–WiFi networks

Providing diverse, ubiquitous and cost-effective broadband services is a foremost challenge for the telecommunication community. Fixed WiMAX or IEEE 802.16d is one of the most promising radio access technologies, providing high performance similar to wired xDSL systems, yet superior to that of current 3G mobile technologies. Numerous deployment concepts are foreseen for WiMAX networks. They are designed to cover isolated areas, thus embodying an appealing solution for cellular networks or wireless backhaul for WiFi access. The latter concept is of interest in this paper that puts forward an analytical model based on the economical aspects to dimension hierarchical WiMax–WiFi networks. The proposed model consists in replacing a finite number of nodes by an equivalent continuum. Its key feature lies in accounting for the effect of interference as well as for the physical layer and channel characteristics in an easy and straightforward manner. On the one hand, the model takes into consideration frequency planning and scheduling aspects; and on the other hand, it provides tractable formulae of the end-user mean capacity and coverage probability in order to properly dimension the hybrid network. Last but not least, the economical facet of network planning is considered to unravel the design trade-offs between maximizing the service provider profit and satisfying the end user requirements in terms of performance.     

Application of hierarchical matrices for computing the Karhunen–Loève expansion

Realistic mathematical models of physical processes contain uncertainties. These models are often described by stochastic differential equations (SDEs) or stochastic partial differential equations (SPDEs) with multiplicative noise. The uncertainties in the right-hand side or the coefficients are represented as random fields. To solve a given SPDE numerically one has to discretise the deterministic operator as well as the stochastic fields. The total dimension of the SPDE is the product of the dimensions of the deterministic part and the stochastic part. To approximate random fields with as few random variables as possible, but still retaining the essential information, the Karhunen–Loève expansion (KLE) becomes important. The KLE of a random field requires the solution of a large eigenvalue problem. Usually it is solved by a Krylov subspace method with a sparse matrix approximation. We demonstrate the use of sparse hierarchical matrix techniques for this. A log-linear computational cost of the matrix-vector product and a log-linear storage requirement yield an efficient and fast discretisation of the random fields presented.     

An hp finite element adaptive scheme to solve the Laplace model for fluid–solid vibrations

In this paper we introduce an hp finite element method to solve a two-dimensional fluid–structure spectral problem. This problem arises from the computation of the vibration modes of a bundle of parallel tubes immersed in an incompressible fluid. We prove the convergence of the method and a priori error estimates for the eigenfunctions and the eigenvalues. We define an a posteriori error estimator of the residual type which can be computed locally from the approximate eigenpair. We show its reliability and efficiency by proving that the estimator is equivalent to the energy norm of the error up to higher order terms, the equivalence constant of the efficiency estimate being suboptimal in that it depends on the polynomial degree. We present an hp adaptive algorithm and several numerical tests which show the performance of the scheme, including some numerical evidence of exponential convergence.     

Bridging proper orthogonal decomposition methods and augmented Newton–Krylov algorithms: An adaptive model order reduction for highly nonlinear mechanical problems

This article describes a bridge between POD-based model order reduction techniques and the classical Newton/Krylov solvers. This bridge is used to derive an efficient algorithm to correct, “on-the-fly”, the reduced order modelling of highly nonlinear problems undergoing strong topological changes. Damage initiation problems tackled via a corrected hyperreduction method are used as an example. It is shown that the relevancy of reduced order model can be significantly improved with reasonable additional costs when using this algorithm, even when strong topological changes are involved.     

An input‐output approach to achieve adaptive tracking for saturating stable systems

The problem of controlling time-invariant linear systems subject to parametric uncertainty and input saturation constraint, is dealt with using a pole placement adaptive controller. It is shown that the resulting adaptive control system can be described by a non-linear feedback scheme. Then, the l 2 -stability condition for the control system turns out to be the positive-realness of a transfer function involving the plant (estimated) model and the specified closed-loop poles. This makes it necessary to adapt on-line the desired closed-loop poles to the time-varying (estimated) model. The proposed adaptive controller keeps bounded all the closed loop sequences, whatever the initial conditions. Furthermore, it ensures a quite interesting output-reference tracking behaviour. More precisely, the quality of the tracking depends on the reference sequence rate: the slower the reference the better the tracking.     

An eye–hand data fusion framework for pervasive sensing of surgical activities

This paper describes a generic framework for activity recognition based on temporal signals acquired from multiple input modalities and demonstrates its use for eye–hand data fusion. As a part of the data fusion framework, we present a multi-objective Bayesian Framework for Feature Selection with a pruned-tree search algorithm for finding the optimal feature set(s) in a computationally efficient manner. Experiments on endoscopic surgical episode recognition are used to investigate the potential of using eye-tracking for pervasive monitoring of surgical operation and to demonstrate how additional information induced by hand motion can further enhance the recognition accuracy. With the proposed multi-objective BFFS algorithm, suitable feature sets both in terms of feature relevancy and redundancy can be identified with a minimal number of instruments being tracked.     

An Introduction to Eulerian Geometrical Optics (1992–2002)

This document is an attempt at introducing the different Eulerian numerical methods which have recently been developed for the simulation of geometric optics and related models.     

A tensor-network-based big data fusion framework for Cyber–Physical–Social Systems (CPSS)

Big data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many risks and challenges. One major challenge is how to represent and treat higher-order and heterogeneous data from multi-sources. Tensors are emerging as powerful tools for representation and modeling of this data. Tensor decomposition can be used to extract potentially useful information from this data. Thus, it has attracted much attention from the big data community. The main target of this paper is to propose a novel data fusion framework to solve several main challenges of CPSS data applications, including CPSS big data representation, fusion, efficient computing, storage, robustness, and security issues. In this paper, we use many graphics to visualize complex tensor decomposition and transformation processes. The visualization may help the readers better understand tensor and tensor decomposition. It also provides a general guideline and a good starting point for those who are interested in tensor and tensor decomposition. Specifically, we first introduce the most extensively used matrix and tensor decomposition methods. Second, the current popular data fusion methods are reviewed and summarized. Third, we propose a novel tensor-network-conversion-based data fusion approach which can simultaneously analyze multiple matrices and multiple tensors. To better understand this approach, we give a brief review of tensor network. Fourth, based on the proposed approach, a novel CPSS big data fusion framework is proposed in this paper. Meanwhile, we verify it by a concise case study. Finally, some challenges and open problems of the proposed framework are discussed. The discussion also includes some exciting future research directions in the big data fusion field.     

Tracker-aware adaptive detection: An efficient closed-form solution for the Neyman–Pearson case

A promising line of research for radar systems attempts to optimize the detector thresholds so as to maximize the overall performance of a radar detector–tracker pair. In the present work, we attempt to move in a direction to fulfill this promise by considering a particular dynamic optimization scheme which relies on a non-simulation performance prediction (NSPP) methodology for the probabilistic data association filter (PDAF), namely, the modified Riccati equation (MRE). By using a suitable functional approximation, we propose a closed-form solution for the special case of a Neyman–Pearson (NP) detector. The proposed solution replaces previously proposed iterative solution formulations and results in dramatic improvement in computational complexity without sacrificed system performance. Moreover, it provides a theoretical lower bound on the detection signal-to-noise ratio (SNR) concerning when the whole tracking system should be switched to the track before detect (TBD) mode.     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

An adaptive compensation scheme-based coded direct detection optical–orthogonal frequency division multiplex (OFDM) system

A major concern in the use of optical-OFDM systems is their limited dynamic range due to nonlinear distortion (NLD) and high peak to average power ratio (PAPR). In this paper, an adaptive filter–based compensation scheme is proposed for a coded direct detected-optical-OFDM system. Moreover, different turbo code rates (DTCRs) are applied for the subcarriers of different regions. For subcarriers in low and high frequency regions a lower rate turbo codes are used. On the other hand for remaining subcarriers a higher rate turbo codes are preferred. This kind of system helps to provide high receiver sensitivity and improved Bit error rate (BER) by more than 2 dB.     

An integrated,programming model-driven framework for NoC–QoS support in cluster-based embedded many-cores

Embedded SoC designs are embracing the many-core paradigm to deliver the required performance to run an ever-increasing number of applications in parallel. Networks-on-Chip (NoC) are considered as a convenient technology to implement many-core embedded platforms. The complex and non-uniform nature of the traffic flows generated when multiple parallel applications are running simultaneously calls for Quality-of-Service (QoS) extensions in the NoC, but to efficiently exploit similar services it is necessary to expose them to the software in a easy-to-use yet efficient manner. In this paper we present an integrated hardware/software approach for delivering QoS on top of an hybrid OpenMP-MPI parallel programming model. Our experimental results show the effectiveness of our proposal over a broad range of benchmarks and application mappings, demonstrating the ability to manage parallelism under QoS requirements effortlessly from the programming model.     

An adaptive XIGA with locally refined NURBS for modeling cracked composite FG Mindlin–Reissner plates

Engineering with Computers - This paper is devoted to numerical investigations on mechanical behavior of cracked composite functionally graded (FG) plates. We thus develop an efficient adaptive...     

An adaptive observer for a spacecraft–manipulator system using a camera mounted on its spacecraft

This paper deals with a motion control system for a space robot with a manipulator. Many motion controllers require the positions of the robot body and the manipulator hand with respect to an inertial coordinate system. In order to measure them, a visual sensor using a camera is frequently used. However, there are two difficulties in measuring them by means of a camera. The first one is that a camera is mounted on the robot body, and hence it is difficult to directly measure the position of the robot body by means of it. The second one is that the sampling period of a vision system with a general-purpose camera is much longer than that of a general servo system. In this paper, we develop an adaptive state observer that overcomes the two difficulties. In order to investigate its performance, we design a motion control system that is constructed by combining the observer with a PD control input, and then conduct numerical simulations for the control system. Simulation results demonstrate the effectiveness of the proposed observer.     

Comments on “Wirtinger-based integral inequality: Application to time-delay systems [Automatica 49 (2013) 2860–2866]”

In a recent paper (Automatica 49 (2013) 2860–2866), the Wirtinger-based inequality has been introduced to derive tractable stability conditions for time-delay or sampled-data systems. We point out that there exist two errors in Theorem 8 for the stability analysis of sampled-data systems, and the correct theorem is presented.     

How to organize for AI? An interview with Yao-Hua Tan

Electronic Markets -