A cellular neural network methodology for deformable object simulation.

This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extrapolating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation.     

Image-adaptive watermarking using 2D chirps

Linear chirps, a special case of polynomial phase exponentials, have recently been proposed for digital watermarking. In this work, we propose a known-host-state methodology for designing image watermarks that are robust to compression. We use a two-dimensional frequency-modulated chirp as a spreading function in a block-based spatial watermarking scheme. In each block, the chirp is used to embed binary phase information. Chirp parameters allow for spectral shaping of the watermark to match host content. Since host state is known to the embedder, it is possible to tune the chirp for optimum performance, particularly against compression. In contrast to existing chirp watermarking where only a single watermark is generally embedded, the proposed block chirp watermarking allows for a much higher payload. Detection is done using chirp transform subject to key exchange for security. We show that the proposed method significantly outperforms non-adaptive watermarking across all compression factors under variety of attacks.     

Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream

Peer-to-Peer (P2P) applications generate streaming data in large volumes, where new communities of peers regularly attend and existing communities of peers regularly leave, requiring the classification techniques to consider concept drift, and update the model incrementally. Concept-adapting Very Fast Decision Tree (CVFDT) is one of the well-known streaming data mining techniques that can be applied to P2P traffic. However, we observe that P2P traffic data is class imbalanced, namely, only about 30 % of examples can be labeled as “P2P”, biasing the trained models (e.g. decision tree) towards the majority class (i.e. “NonP2P”). In this paper, we propose a new technique, the imbalanced CVFDT (iCVFDT), by integrating the CVFDT with an efficient resampling technique to address the issue of the class imbalanced data. The iCVFDT classification technique enjoys the advantages of CVFDT (such as stability), and at the same time, is not sensitive to imbalanced data. We captured the Internet traffic at a main gateway and prepared a real data stream with 3.5 million examples to which the iCVFDT classification technique was applied. The experimental results demonstrate a significant improvement in the performance of the iCVFDT compared to that of the CVFDT.     

Shape optimization of geotextile tubes for sandy beach protection

This paper describes how to tackle new challenging coastal engineering problems related to beach erosion with a shape optimization approach. The method modifies the shape of the sea bottom in order to reduce beach erosion effects. Global optimization is shown to be necessary as the related functionals have several local minima. We describe the physical model used, the proposed protection devices against beach erosion and real case applications. Copyright © 2007 John Wiley & Sons, Ltd.     

Incomplete sensitivities for 3D radial turbomachinery blade optimization

We are interested in optimal design of 3D complex geometries, such as radial turbomachines, in large control space. The calculation of the gradient of the cost function is a key point when a gradient based method is used. Finite difference method has a complexity proportional to the size of the control space and the adjoint method requires important extra coding. We propose to consider the incomplete sensitivities method for optimal design of radial turbomachinery blades. The central point of the paper is how to adapt some formulations in radial turbomachinery to the validity domain of incomplete sensitivities. Also, we discuss on how to improve the accuracy of incomplete sensitivities using reduced order models based on physical assumptions. Fine/Turbo flow solver is coupled with gradient based optimization algorithms based on CAD-connected frameworks. Newton methods together with incomplete expressions of gradients are used. The approach is validated through optimization of centrifugal pumps. Finally the results are considered and discussed.     

Fault-Based Attack on Montgomery��s Ladder Algorithm

In this paper we present invalid-curve attacks that apply to the Montgomery ladder elliptic curve scalar multiplication (ECSM) algorithm. An elliptic curve over the binary field is defined using two parameters, a and b. We show that with a different “value” for curve parameter a, there exists a cryptographically weaker group in nine of the ten NIST-recommended elliptic curves over \mathbbF_2^m\mathbb{F}_{2^{m}}. Thereafter, we present two attacks that are based on the observation that parameter a is not utilized for the Montgomery ladder algorithms proposed by López and Dahab (CHES 1999: Cryptographic Hardware and Embedded Systems, LNCS, vol. 1717, pp. 316–327, Springer, Berlin, 1999). We also present the probability of success of such attacks for general and NIST-recommended elliptic curves. In addition we give some countermeasures to resist these attacks.     

Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly

A new method of evaluating the characteristics of postural transition (PT) and their correlation with falling risk in elderly people is described. The time of sit-to-stand and stand-to-sit transitions and their duration were measured using a miniature gyroscope attached to the chest and a portable recorder placed on the waist. Based on a simple model and the discrete wavelet transform, three parameters related to the PT were measured, namely, the average and standard deviation of transition duration and the occurrence of abnormal successive transitions (number of attempts to have a successful transition). The comparison between two groups of elderly subjects (with high and low fall-risk) showed that the computed parameters were significantly correlated with the falling risk as determined by the record of falls during the previous year, balance and gait disorders (Tinetti score), visual disorders, and cognitive and depressive disorders (p < 0.01). In this study, the wavelet transform has provided a powerful technique for enhancing the pattern of PT, which was mainly concentrated into the frequency range of 0.04-0.68 Hz. The system is especially adapted for long-term ambulatory monitoring of elderly people.     

A connection control strategy for bursty sources in broadband packet networks

This paper proposes an admission strategy for connection-oriented services at the access node of a broadband integrated packet network based on asynchronous transfer mode. Based on an estimate of the probability of cell loss and in the absence of buffering, we determine the number of sources from different classes of bursty traffic which can be accepted. The parameters which are used to describe the traffic sources are average bit rate and peak bit rate. We will evaluate the cell loss estimate for homogeneous and heterogeneous cases. Furthermore, we will examine the variation of this estimate as the average to peak ratio changes. The estimate is an upper bound for the probability of cell loss. Based on this upper bound simple and robust allocation of resources to bursty sources can be developed. The proposed strategy can be implemented using simple computations or via a look-up table to determine admission or denial of an incoming call and therefore allocating the required capacity. The procedure results in efficient use of the bandwidth, ensuring the desired service quality for connection-oriented services and results in proactive control of network congestion.     

Structured Multiblock Grid Solution of Two-Dimensional Transient Inverse Heat Conduction Problems in Cartesian Coordinate System

ABSTRACT

In this study a structured multiblock grid is used to solve two-dimensional transient inverse heat conduction problems. The multiblock method is implemented for geometric decomposition of the physical domain into regions with blocked interfaces. The finite-element method is employed for direct solution of the transient heat conduction equation in a Cartesian coordinate system. Inverse algorithms used in this research are iterative Levenberg-Marquardt and adjoint conjugate gradient techniques for parameter and function estimations. The measured transient temperature data needed in the inverse solution are given by exact or noisy data. Simultaneous estimation of unknown linear/nonlinear time-varying strengths of two heat sources in two joined surfaces with equal and different heights is obtained for the solution of the inverse problems, and the results of the present study for unknown heat source functions are compared to those of exact functions. This study is an attempt to challenge the goal of combining a multiblock technique with inverse analysis methods. In fact, the structured multiblock grid is capable of providing accurate solutions of inverse heat conduction problems (IHCPs) in industrial configurations, including composite structures. In addition, the multiblock IHCP solver is suitable to estimate unknown parameters and functions in these structures.