Using I2P (Invisible Internet Protocol) Encrypted Virtual Tunnels for a Secure and Anonymous Communication in VANets: I2P Vehicular Protocol (IVP)

Wireless Personal Communications - The Invisible Internet Project (I2P) as a secure protocol uses robust mechanisms and strong algorithms to reinforce the security and the anonymity of the...     

Comparison of the identification performance of conventional FEM updating and integrated DIC

Full‐field identification methods are increasingly used to adequately identify constitutive parameters to describe the mechanical behavior of materials. This paper investigates the more recently introduced one‐step method of integrated digital image correlation (IDIC) with respect to the most commonly used two‐step method of finite element model updating (FEMU), which uses a subset‐based DIC algorithm. To make the comparison as objective as possible, both methods are implemented in the most equivalent manner and use the same FE model. Various virtual test cases are studied to assess the performance of both methods when subjected to different error sources: (1) systematic errors, (2) poor initial guesses for the constitutive parameters, (3) image noise, (4) constitutive model errors, and (5) experimental errors. Results show that, despite the mathematical similarity of both methods, IDIC produces less erroneous and more reliable results than FEMU, particularly for more challenging test cases exhibiting small displacements, complex kinematics, misalignment of the specimen, and image noise. Copyright © 2015 John Wiley & Sons, Ltd.     

Noncontact Sonic NDE and Defect Imaging Via Local Defect Resonance

A selective acoustic activation of defects based on the concept of local defect resonance enables to enhance considerably the intensity of defect vibrations and makes it possible to reduce the input acoustic powers to the levels permissible for noncontact nondestructive inspection. Since for cm-size defects in composite materials, the LDR frequencies lie in the low kHz-range, the resonant noncontact activation shifts to an audible frequency range and can be provided by conventional sonic equipment. In this paper, the feasibility of the resonant noncontact inspection is validated for the most “problematic” methodologies of nonlinear, thermosonic and shearosonic NDE that usually require an elevated acoustic power and, therefore, a reliable contact between the specimen and the transducer. In contrast, the noncontact versions developed employ commercial loudspeakers which can simultaneously insonify large areas and be applied for a contactless sonic inspection of different materials and various scale components.     

Formation of Cu Nanodots on Diamond Surface to Improve Heat Transfer in Cu/D Composites

Diamond‐dispersed copper matrix (Cu/D) composite materials with different interfacial configurations are fabricated through powder metallurgy and their thermal performances are evaluated. An innovative solution to chemically bond copper (Cu) to diamond (D) has been investigated and compared to the traditional Cu/D bonding process involving carbide‐forming additives such as boron (B) or chromium (Cr). The proposed solution consists of coating diamond reinforcements with Cu particles through a gas–solid nucleation and growth process. The Cu particle‐coating acts as a chemical bonding agent at the Cu–D interface during hot pressing, leading to cohesive and thermally conductive Cu/D composites with no carbide‐forming additives. Investigation of the microstructure of the Cu/D materials through scanning electron microscopy, transmission electron microscopy, and atomic force microscopy analyses is coupled with thermal performance evaluations through thermal diffusivity, dilatometry, and thermal cycling. Cu/D composites fabricated with 40 vol% of Cu‐coated diamonds exhibit a thermal conductivity of 475 W m^?1 K^?1 and a thermal expansion coefficient of 12 × 10^?6 °C^?1. These promising thermal performances are superior to that of B‐carbide‐bonded Cu/D composites and similar to that of Cr‐carbide‐bonded Cu/D composites fabricated in this study. Moreover, the Cu/D composites fabricated with Cu‐coated diamonds exhibit higher thermal cycling resistance than carbide‐bonded materials, which are affected by the brittleness of the carbide interphase upon repeated heating and cooling cycles. The as‐developed materials can be applicable as heat spreaders for thermal management of power electronic packages. The copper‐carbon chemical bonding solution proposed in this article may also be found interesting to other areas of electronic packaging, such as brazing solders, direct bonded copper substrates, and polymer coatings.

     

Magnetic Properties of Thermal Sprayed Tungsten Carbide–Cobalt Coatings

In the information age, the storage and accessibility of data is of vital importance. There are several possibilities to fulfill this task. Magnetic storage of data is a well‐established method and the range of materials used is continuously extended. In this study, the magnetic remanence of thermally sprayed tungsten carbide–cobalt (WCCo)‐coatings in dependence of their thickness is examined. Two magnetic fields differing in value and geometry are imprinted into the coatings and the resulting remanence field is measured. It is found that there are two effects, which in combination determine the effective value of the magnetic remanence usable for magnetic data storage.

     

A fully pipelined RLS-based array for channel equalization

It is shown that several recursive least squares (RLS) type equalization algorithms such as, e.g., decisiondirected schemes and orthogonalized constant modulus algorithms, possess a common algorithmic structure and are therefore rather straightforwardly implemented on an triangular array (filter structure) for RLS estimation with inverse updating. While the computational complexity for such algorithms isO(N ²), whereN is the problem size, the throughput rate for the array implementation isO(1), i.e., independent of the problem size. Such a throughput rate cannot be achieved with standard (Gentleman-Kung-type) RLS/QR-updating arrays because of feedback loops in the computational schemes.     

Controlled counter-flow motion of magnetic bead chains rolling along microchannels

We demonstrate controlled transport of superparamagnetic beads in the opposite direction of a laminar flow. A permanent magnet assembles 200 nm magnetic particles into about 200 μm long bead chains that are aligned in parallel to the magnetic field lines. Due to a magnetic field gradient, the bead chains are attracted towards the wall of a microfluidic channel. A rotation of the permanent magnet results in a rotation of the bead chains in the opposite direction to the magnet. Due to friction on the surface, the bead chains roll along the channel wall, even in counter-flow direction, up to at a maximum counter-flow velocity of 8 mm s⁻¹. Based on this approach, magnetic beads can be accurately manoeuvred within microfluidic channels. This counter-flow motion can be efficiently be used in Lab-on-a-Chip systems, e.g. for implementing washing steps in DNA purification.     

Cost optimization of a hybrid composite flywheel rotor with a split-type hub using combined analytical/numerical models

A procedure to find the optimal design of a flywheel with a split-type hub is presented. Since cost plays a decisive role in stationary flywheel energy storage applications, a trade-off between energy and cost is required. Applying a scaling technique, the multi-objective design problem is reduced to the maximization of the energy-per-cost ratio as the single objective. Both an analytical and a finite element model were studied. The latter was found to be more than three orders of magnitude more computationally expensive than the analytical model, while the analytical model can only be regarded as a coarse approximation. Multifidelity approaches were examined to reduce the computational expense while retaining the high accuracy and large modeling depth of the finite element model. Using a surrogate-based optimization strategy, the computational cost was reduced to only one third in comparison to using only the finite element model. A nonlinear interior-point method was employed to find the optimal rim thicknesses and rotational speed. The benefits of the split-type hub architecture were demonstrated.     

Learning state machine-based string edit kernels

During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^′ built from an alphabet Σ requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over Σ^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.