Markovian-based traffic modeling for mobile ad hoc networks

Mobile ad hoc networks (MANETs) show very significant difference with respect to other computer networks due to the presence of extremely large packet loss bursts. The development of protocols for mobile ad hoc networks, especially multimedia protocols, require extensive evaluation either through simulation or real-life tests. Such testing consumes a great amount of resources both in terms of time and trace file sizes. Therefore, finding efficient means of reducing the amount of data that is stored and processed is quite important to accelerate the evaluation of different audio/video streaming applications. If, moreover, we are able to model the loss pattern experienced, we can further accelerate the evaluation process.In this work we propose two models based on hidden Markov chains that are able to grasp both packet arrivals and packet loss patterns in MANETs. A simpler two-state model is proposed to model losses when proactive routing protocols are used, while a more complex three-state model is proposed for reactive routing protocols. We also introduce a new set for packet loss pattern measurements that can be of interest for the evaluation of audio/video streaming applications.Experimental results show that the proposed models can adequately reproduce extremely long packet loss patterns, typical of MANET environments, with a high degree of accuracy. Overall, we find that the proposed models are able to significantly reduce both the simulation time and the trace file sizes required.     

Soft computing and cooperative strategies for optimization

In the last years, metaheuristics have emerged as powerful algorithmic approaches which have been applied with great success to difficult combinatorial optimization problems. However, this does not mean that metaheuristics can be applied blindly to any new problem.In this contribution we showed how the most basic ingredients of Soft Computing, namely fuzzy sets and fuzzy rules, are used in the context of a simple metaheuristic and a cooperative strategy based on it, to obtain successful results for the p-median problem.     

A new control scheme for visual servoing

This research presents a new control scheme for visual servoing that takes into account the delay introduced by image acquisition and image processing. Firstly, previous control schemes for visual servoing are discussed and then a new control scheme is proposed and fully described. Afterwards, the capabilities (steady-state errors, stability margins, step time response, etc.) of the proposed control scheme and of previous ones are analytically analyzed and compared. Next, several simulations and experimental results are provided to validate the analytical results and to illustrate the benefits of the proposed control scheme. In particular, it is shown that this new control scheme clearly improves the performance of the previous ones.     

Testing the hypothesis of a block compound symmetric covariance matrix for elliptically contoured distributions

In this paper, the authors study the problem of testing the hypothesis of a block compound symmetry covariance matrix with two-level multivariate observations, taken for m variables over u sites or time points. Through the use of a suitable block-diagonalization of the hypothesis matrix, it is possible to obtain a decomposition of the main hypothesis into two sub-hypotheses. Using this decomposition, it is then possible to obtain the likelihood ratio test statistic as well as its exact moments in a much simpler way. The exact distribution of the likelihood ratio test statistic is then analyzed. Because this distribution is quite elaborate, yielding a non-manageable distribution function, a manageable but very precise near-exact distribution is developed. Numerical studies conducted to evaluate the closeness between this near-exact distribution and the exact distribution show the very good performance of this approximation even for very small sample sizes and the approach followed allows us to extend its validity to situations where the population distributions are elliptically contoured. A real-data example is presented and a simulation study is also conducted.     

Design Optimization of Composite Prosthetic Tubes Using GA-ANN Algorithm Considering Tsai-Wu Failure Criteria

The investigation of possible failures in composite materials is a matter of very great importance, and the Tsai-Wu criterion is an effective criterion for analyzing those flaws in anisotropic materials and defining whether the material at a given load will or will not suffer structural failure. In this study, an optimization procedure is proposed to minimize the maximum value of Tsai-Wu of laminated composite tubes subject to axial loading. Artificial neural networks and genetic algorithms are chosen as optimization tools. The results of this study show that the developed algorithm converges faster. Then, the maximum Tsai-Wu value is used as the objective function and the fiber orientations are the constraints in the optimization process. The results yielded by them are compared and discussed. Optimal results are compared with respect to the usual initial design. The design approach is recommended for structures where composites are the key load-carrying members such as orthopedic prosthesis.     

On the Structure of Amorphous Mesoporous Silica Nanoparticles by Aberration‐Corrected STEM

Mesoporous silica materials have demonstrated a vast spectrum of applications, stimulating an intensive field of study due to their potential use as nanocarriers. Nonetheless, when produced at the nanoscale, their structural characterization is hindered due to the re‐arrangement of the pores. To address this issue, this work combines molecular dynamics simulations with electron microscopy computer simulations and experimental results to provide an insight into the structure of amorphous mesoporous silica nanoparticles. The amorphous silica model is prepared using a simple melt‐quench molecular dynamics method, while the reconstruction of the mesoporous nanoparticles is carried out using a methodology to avoid false symmetry in the final model. Simulated scanning transmission electron microscopy images are compared with experimental images, revealing the existence of structural domains, created by the misalignment of the pores to compensate the surface tension of these spherical nanoparticles.     

Analysis of bioenergy production at different levels of integration in energy-driven biorefineries

In this paper, a techno-economic and environmental assessment is performed to compare the stand-alone process and biorefinery ways to produce biodiesel, ethanol and butanol as potential cases for bioenergy production using fresh fruit bunches as raw material. Different levels of integration are considered (e.g., mass and energy integration, non-conventional technologies) along with the analysis of the process scale to determine the economic profitability and environmental impacts of the proposed cases. The results demonstrated that the biodiesel production based on the biorefinery concept has a positive effect on the profitability of the stand-alone process at different scales. The economic results were compared with data reported in the literature. Furthermore, the life cycle analysis of the proposed cases suggested that the deployment of the biorefinery concept at different levels of integration in the oil palm supply chain reduced the environmental impact of the biodiesel production, which was selected as the hotspot of the evaluated cases.     

Robust metric calibration of non-linear camera lens distortion

Camera lens distortion is crucial to obtain the best performance cameral model. Up to now, different techniques exist, which try to minimize the calibration error using different lens distortion models or computing them in different ways. Some compute lens distortion camera parameters in the camera calibration process together with the intrinsic and extrinsic ones. Others isolate the lens distortion calibration without using any template and basing the calibration on the deformation in the image of some features of the objects in the scene, like straight lines or circles. These lens distortion techniques which do not use any calibration template can be unstable if a complete camera lens distortion model is computed. They are named non-metric calibration or self-calibration methods.Traditionally a camera has been always best calibrated if metric calibration is done instead of self-calibration. This paper proposes a metric calibration technique which computes the camera lens distortion isolated from the camera calibration process under stable conditions, independently of the computed lens distortion model or the number of parameters. To make it easier to resolve, this metric technique uses the same calibration template that will be used afterwards for the calibration process. Therefore, the best performance of the camera lens distortion calibration process is achieved, which is transferred directly to the camera calibration process.     

Projective active shape models for pose-variant image analysis of quasi-planar objects: Application to facial analysis

One of the important obstacles in the image-based analysis of the human face is the 3D nature of the problem and the 2D nature of most imaging systems used for biometric applications. Due to this, accuracy is strongly influenced by the viewpoint of the images, being frontal views the most thoroughly studied. However, when fully automatic face analysis systems are designed, capturing frontal-view images cannot be guaranteed. Examples of this situation can be found in surveillance systems, car driver images or whenever there are architectural constraints that prevent from placing a camera frontal to the subject. Taking advantage of the fact that most facial features lie approximately on the same plane, we propose the use of projective geometry across different views. An active shape model constructed with frontal-view images can then be directly applied to the segmentation of pictures taken from other viewpoints. The proposed extension demonstrates being significantly more invariant than the standard approach. Validation of the method is presented in 360 images from the AV@CAR database, systematically divided into three different rotations (to both sides), as well as upper and lower views due to nodding. The presented tests are among the largest quantitative results reported to date in face segmentation under varying poses.