Hop chains: Secure routing and the establishment of distinct identities

We present a secure routing protocol that is immune to Sybil attacks and that can tolerate collusion of Byzantine routers. It can tolerate either initial collusion of Byzantine routers or runtime collusion of non-adjacent Byzantine routers, both in the absence of runtime collusion between adjacent routers. For these settings, the calculated distance from a destination to a node is not smaller than the actual shortest distance from the destination to the node. The protocol can also simultaneously tolerate initial collusion of Byzantine routers and runtime collusion of adjacent Byzantine routers but in the absence of runtime collusion between non-adjacent routers. For this setting, it guarantees a bound on the difference between the calculated distance and the actual shortest distance. The bound depends on the number of Byzantine routers on a path. The protocol makes very weak timing assumptions and requires synchronization only between neighbors or second neighbors. We propose to use this protocol for secure localization of routers using hop-count distances, which can be then used as a proof of identity of nodes.     

Experimental analysis and numerical modelling of the forming process of polypropylene replicas of micro-cavities using hot embossing

Numerical modelling of the deformation of a polymer using the finite elements method in axisymetrical mode was performed using the LsDyna^® software to describe the filling of micro-cavities during the forming process of the material using the hot embossing. These simulations firstly allow verifying whether the chosen forming process conditions promote or not an optimized filling of the superficial cavities in order to achieve precise replicas which best reproduce the superficial topography of the mould. The simulations were carried out to evaluate the filling of the cavities taking into account the mechanical behaviour of the selected polymer into the model. Moreover, these models were developed to verify the effect of the distribution of the mould cavities on their filling. The influence of the mobility of non deformable rigid plates on the filling of the cavities represents an auxiliary variable. In the approach presented, the compression plates are assumed to be parallel and non deformable, whereas the polymer disk follows a rubbery behaviour around a temperature equal to 140°C. Globally the modelling results are satisfactory for they are rather close to the experimental observations conducted. In summary, the effect of the normal stress as also the distribution of micro-cavities at the mould surface seem to prevail in the case of the forming process by hot embossing.     

Rapid real-time generation of super-resolution hyperspectral images through compressive sensing and GPU

Recently, compressive sensing (CS) has offered a new framework whereby a signal can be recovered from a small number of noisy non-adaptive samples. This is now an active area of research in many image-processing applications, especially super-resolution. CS algorithms are widely known to be computationally expensive. This paper studies a real time super-resolution reconstruction method based on the compressive sampling matching pursuit (CoSaMP) algorithm for hyperspectral images. CoSaMP is an iterative compressive sensing method based on the orthogonal matching pursuit (OMP). Multi-spectral images record enormous volumes of data that are required in practical modern remote-sensing applications. A proposed implementation based on the graphical processing unit (GPU) has been developed for CoSaMP using computed unified device architecture (CUDA) and the cuBLAS library. The CoSaMP algorithm is divided into interdependent parts with respect to complexity and potential for parallelization. The proposed implementation is evaluated in terms of reconstruction error for different state-of-the-art super-resolution methods. Various experiments were conducted using real hyperspectral images collected by Earth Observing-1 (EO-1), and experimental results demonstrate the speeding up of the proposed GPU implementation and compare it to the sequential CPU implementation and state-of-the-art techniques. The speeding up of the GPU-based implementation is up to approximately 70 times faster than the corresponding optimized CPU.     

Modeling of mechanical resonators used for nanocrystalline materials characterization and disease diagnosis of HIVs

The modeling and performance of mechanical resonators used for mass detection of bio-cells, nanocrystalline materials characterization, and disease diagnosis of human immune-viruses (HIVs) are investigated. To simulate the real behavior of these mechanical resonators, a novel distributed-parameter model based on Euler–Bernoulli beam theory is developed. This model is equipped with a micromechanical model and an atomic lattice model to capture the inhomogeneity nature of the material microstructure. Compared with lumped-parameter model predictions, the results show that this developed model best fits with the real behavior of the mechanical resonators when detecting the mass of vaccinia virus. In terms of material characterization, the developed model gives very good estimations for the densities and Young’s moduli of the grain boundary of both the nanocrystalline silicon and nanocrystalline diamond. For disease diagnosis, it is shown that the number of human immune-deficiency virus particles in a liquid sample can be easily detected when using the proposed model. The results also show that the developed model is beneficial and can be used to design mechanical resonators made of nanocrystalline materials with the ability to control the resonators’ sizes and the material structure.     

Novel nano polymeric system containing biosynthesized core shell silver/silica nanoparticles for functionalization of cellulosic based material

An innovative strategy for functional finishing of cellulosic based materials is based on the incorporation of a thin layer of surface modifying systems (SMS) in the form of stimuli-sensitive nanogels containing combining metal nanoparticles and silica. The silver–silica core–shell nanoparticles (NPs) were synthesized by simple one pot chemical method. Silica/silver nanoparticles have been synthesized using low concentration of dextran as reducing and stabilizing agent and using ascorbic acid as antioxidant agent. The core–shell NPs were characterized for their structural, morphological, compositional and optical behaviour using X-ray diffraction, scanning electron microscopy and energy dispersive X-ray analysis. Stimuli-responsive nanogel was prepared by copolymerization of poly(N-isopropylacrylamide) with pullulan, results in a nanogel that is responsive to both temperature and pH, the nano-particulate hydrogel of poly-NiPAAm-pullulan copolymer was synthesized using surfactant-free emulsion method. The prepared nano-particles were used during the preparation steps of the pullulan nanogel to obtain nanogel/combining metal/silica NPs to produce a composite materials. The nanoparticle size in dry (collapsed) state is estimated at 250 nm by SEM and TEM, and effect of temperature and pH on gel-nanoparticles was investigated by DLS and UV–vis spectrophotometry. The incorporation of the nanoparticles to cellulosic material was done by a simple pad dry-cure procedure from aqueous nanoparticle dispersion that contained a cross-linking agent. This application method provided sufficient integrity to coating by maintaining the responsiveness of surface modifying system. The stimuli responsiveness of modified cellulosic materials has been confirmed in terms of regulating its water uptake in dependence of pH and temperature.     

Modeling and simulation of structural components in recursive closed-loop multibody systems

Passenger cars, transit buses, railroad vehicles, off-highway trucks, earth moving equipment and construction machinery contain structural and light-fabrications (SALF) components that are prone to excessive vibration due to rough terrains and work-cycle loads’ excitations. SALF components are typically modeled as flexible components in the multibody system allowing the analysts to predict elastic deformation and hence the stress levels under different loading conditions. Including SALF component in the multibody system typically generates closed-kinematic loops. This paper presents an approach for integrating SALF modeling capabilities as a flexible body in a general-purpose multibody dynamics solver that is based on joint-coordinates formulation with the ability to handle closed-kinematic loops. The spatial algebra notation is employed in deriving the spatial multibody dynamics equations of motion. The system kinematic topology matrix is used to project the Cartesian quantities into the joint subspace, leading to a condensed set of nonlinear equations with minimum number of generalized coordinates. The proposed flexible body formulation utilizes the component mode synthesis approach to reduce the large number of finite element degrees of freedom to a small set of generalized modal coordinates. The resulting reduced flexible body model has two main characteristics: the stiffness matrix is constant while the mass matrix depends on the elastic modal coordinates. A consistent set of pre-computed inertia shape integrals are identified and used to update the modal mass matrix at each time step. The implementation of the component mode synthesis approach in a closed-loop recursive multibody formulation is presented. The kinematic equations are modified to include the effect of the flexible body modal elastic coordinates. Also, modified constraint equations that include the effect of flexibility at the joint connections and the necessary details of the Jacobian matrix are presented. Baumgarte stabilization approach is used to stabilize the constraint equations without using iterative schemes. A sample results for flexible body impeded in a closed system will be presented to demonstrate the above mentioned approach.     

Efficient schemes for similarity-aware refinement of aggregation queries

Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prior knowledge of data interact with these platforms in an exploratory manner hoping they might retrieve the results they are looking for. One way to explore large-volume data is by posing aggregate queries which group values of multiple rows by an aggregate operator to form a single value: an aggregated value. Though, when a query fails, i.e., returns undesired aggregated value, users will have to undertake a frustrating trial-and-error process to refine their queries, until a desired result is attained. This data exploration process, however, is growing rather difficult as the underlying data is typically of large-volume and high-dimensionality. While heuristic-based techniques are fairly successful in generating refined queries that meet specified requirements on the aggregated values, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is rather a non-trivial challenge, as it requires a careful examination of the query space while maintaining a low processing cost. To address this challenge, we propose an innovative scheme for efficient Similarity-Aware Refinement of Aggregation Queries called (EAGER) which aims to balance the tradeoff between satisfying the aggregate and similarity constraints imposed on the refined query to maximize its overall benefit to the user. To achieve that goal, EAGER implements efficient strategies to minimize the costs incurred in exploring the available search space by utilizing similarity-based and monotonic-based pruning techniques to bound the search space and quickly find a refined query that meets users’ expectations. Our extensive experiments show the scalability exhibited by EAGER under various workload settings, and the significant benefits it provides.     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

Help-Training for semi-supervised support vector machines

In this paper, we propose to reinforce the Self-Training strategy in semi-supervised mode by using a generative classifier that may help to train the main discriminative classifier to label the unlabeled data. We call this semi-supervised strategy Help-Training and apply it to training kernel machine classifiers as support vector machines (SVMs) and as least squares support vector machines. In addition, we propose a model selection strategy for semi-supervised training. Experimental results on both artificial and real problems demonstrate that Help-Training outperforms significantly the standard Self-Training. Moreover, compared to other semi-supervised methods developed for SVMs, our Help-Training strategy often gives the lowest error rate.