Multi-degree reduction of NURBS curves based on their explicit matrix representation and polynomial approximation theory

NURBS curve is one of the most commonly used tools in CAD systems and geometric modeling for its various specialties, which means that its shape is locally adjustable as well as its continuity order, and it can represent a conic curve precisely. But how to do degree reduction of NURBS curves in a fast and efficient way still remains a puzzling problem. By applying the theory of the best uniform approximation of Chebyshev polynomials and the explicit matrix representation of NURBS curves, this paper gives the necessary and sufficient condition for degree reducible NURBS curves in an explicit form. And a new way of doing degree reduction of NURBS curves is also presented, including the multi-degree reduction of a NURBS curve on each knot span and the multi-degree reduction of a whole NURBS curve. This method is easy to carry out, and only involves simple calculations. It provides a new way of doing degree reduction of NURBS curves, which can be widely used in computer graphics and industrial design.     

Local tangent space alignment based on Hilbert–Schmidt independence criterion regularization

Pattern Analysis and Applications - Local tangent space alignment (LTSA) is a famous manifold learning algorithm, and many other manifold learning algorithms are developed based on LTSA. However,...     

Transformation of feature space based on Fisher’s linear discriminant

Linear transformation of data in multidimensional feature space based on Fisher’s criterion is considered. The case of two classes with arbitrary distributions is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Example offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher’s linear discriminant.     

Time–space complexity of quantum search algorithms in symmetric cryptanalysis: applying to AES and SHA-2

Performance of cryptanalytic quantum search algorithms is mainly inferred from query complexity which hides overhead induced by an implementation. To shed light on quantitative complexity analysis removing hidden factors, we provide a framework for estimating time–space complexity, with carefully accounting for characteristics of target cryptographic functions. Processor and circuit parallelization methods are taken into account, resulting in the time–space trade-off curves in terms of depth and qubit. The method guides how to rank different circuit designs in order of their efficiency. The framework is applied to representative cryptosystems NIST referred to as a guideline for security parameters, reassessing the security strengths of AES and SHA-2.     

A company-office system “Valentine” providing informal communication and personal space based on 3D virtual space and avatars

In this paper we propose a virtual office environment that integrates natural communication and secure private space. The features of this system are the following. (1) This system has a virtual shared room based on the idea of “shared room metaphor” and 3D graphics on an SGI workstation is used for this system. It uses Ethernet media (i.e. real-time audio/video streams). (2) This system implements the field of view of a human by using our “around view” technique. This provides more natural communication between members. (3) “Sound effects” are used to help users feel the presence of other members. For instance, members hear the sound of a door opening when someone logs into our system and the sound of footsteps when someone is walking around our virtual room. (4) At times our system limits the flow of awareness information. A person concentrating on his/her work may not want to perceive excessive awareness of others. To support such situation, we define “awareness space” which restricts the field where other members' awareness is transmitted. Awareness space changes in size with the degree of concentration which is measured through two factors: the movement of a chair and the frequency of keyboard typing. (5) “Headphone metaphor”. A picture of a headphone is attached above a person's image and changes color depending on the degree of concentration. This enables other members to recognize his/her state and can be a criterion as to whether he/she is available to communicate or not. (6) In the virtual space, users are represented as avatars built of 3D polygons and still pictures. The avatars change shape automatically according to the users' action.     

Investigation and improvement of reversible microfluidic devices based on glass–PDMS–glass sandwich configuration

Reversibly assembled microfluidic devices are dismountable and reusable, which is useful for a number of applications such as micro- and nano-device fabrication, surface functionalization, complex cell patterning, and other biological analysis by means of spatial–temporal pattern. However, reversible microfluidic devices fabricated with current standard procedures can only be used for low-pressure applications. Assembling technology based on glass–PDMS–glass sandwich configuration provides an alternative sealing method for reversible microfluidic devices, which can drastically increase the sealing strength of reversibly adhered devices. The improvement mechanism of sealing properties of microfluidic devices based on the sandwich technique has not been fully characterized, hindering further improvement and broad use of this technique. Here, we characterize, for the first time, the effect of various parameters on the sealing strength of reversible PDMS/glass hybrid microfluidic devices, including contact area, PDMS thickness, assembling mode, and external force. To further improve the reversible sealing of glass–PDMS–glass microfluidic devices, we propose a new scheme which exploits mechanical clamping elements to reinforce the sealing strength of glass–PDMS–glass sandwich structures. Using our scheme, the glass–PDMS–glass microchips can survive a pressure up to 400 kPa, which is comparable to the irreversibly bonded PDMS microdevices. We believe that this bonding method may find use in lab-on-a-chip devices, particularly in active high-pressure-driven microfluidic devices.     

Sensitivity analysis and optimization of vehicle–bridge systems based on combined PEM–PIM strategy

This paper presents a new optimization method for coupled vehicle–bridge systems subjected to uneven road surface excitation. The vehicle system is simplified as a multiple rigid-body model and the single-span bridge is modeled as a simply supported Bernoulli–Euler beam. The pseudo-excitation method transforms the random surface roughness into the superposition of a series of deterministic pseudo-harmonic excitations, which enables convenient and accurate computation of first and second order sensitivity information. The precise integration method is used to compute the vertical random vibrations for both the vehicle and the bridge. The sensitivities are used to find the optimal solution, with vehicle ride comfort taken as the objective function. Optimization efficiency and computational accuracy are demonstrated numerically.     

Design of reduced search space strategy based on integration of Nelder–Mead method and pattern search algorithm with application to economic load dispatch problem

This paper presents a novel hybrid technique for the solution of economic load dispatch problem with valve point loading effect using Nelder–Mead (NM) simplex method and pattern search (PS) algorithm. Strength of globalized NM optimization algorithm has been employed to explore the search space for near optimal solution, and PS algorithm is used in combination with a search space reduction strategy, incorporating the principles of selection and stochastic reproduction, to fine-tune the result. The proposed technique has been applied to three different systems having 3, 13 and 40 generating units to demonstrate the application for small to large load dispatch set-up. The efficacy of the design scheme is established from comparison of the results with the state-of-the-art solvers, and it is found that the proposed scheme gives the best result in terms of mean cost while the average computational time is less than most of the reported methods.

     

Effect of alloying on stability of grain boundary in γ phase of the U–Mo and U–Nb systems

U–Mo and U–Nb alloys are both extensively used in nuclear industry. γ phase in U–Mo or U–Nb alloy is a solid solution, being metastable in low temperature region. In this work, the effect of alloying on stability of grain boundary in meta-stable γ phase in U–Mo and U–Nb alloys are investigate through first-principles calculations. At first, crystal structure and elastic constants of Mo, Nb and γ-U metals are calculated and the obtain results show the mechanical unstable nature of γ phase at 0 K, no matter with GGA or GGA + U treatment, which agrees with most of the theoretical results in the literature. Furthermore, from the calculated symmetric tilt grain boundary (STGB) formation energies of Σ3[110]/(111) and Σ5[001]/(310) in Mo, Nb, and γ-U, it is found that due to the mechanical unstable character of the γ-U phase, negative GB formation energy is predicted at 0 K for Σ5[001]/(310) if the STGB model is relaxed with all degrees of freedom. Therefore, by using special quasirandom structure (SQS) method, Σ5[001]/(310) and Σ3[110]/(111) STGBs with different solute concentrations in U-rich side in U–Mo and U–Nb systems are further investigated. It is found that, when alloying with Mo or Nb, unlike Σ3[110]/(111), although the fixed-atom constraint is applied, the GB formation energy of Σ5[001]/(310) STGB is becoming negative when the solute concentration is in U-rich side. Only when the concentration of Mo or Nb is larger than 27 at.% or 30 at.%, respectively, or sufficient small, the GB formation energy is becoming positive, suggesting a cooperative effects of solute concentration, unstable character, and grain size on GB structures in γ phase. The predicted different stability of alloyed GB structures at 0 K suggest that although γ phase is metastable at low temperature, its metastability can be controlled through alloying with different solutes, or with different GBs. And grain refinement should be relatively easy in U-rich part than U-poor part of the U–Mo and U–Nb systems.     

Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation

Unimodal analysis of palmprint and palm vein has been investigated for person recognition. One of the problems with unimodality is that the unimodal biometric is less accurate and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multimodal personal identification system using palmprint and palm vein images with their fusion applied at the image level. The palmprint and palm vein images are fused by a new edge-preserving and contrast-enhancing wavelet fusion method in which the modified multiscale edges of the palmprint and palm vein images are combined. We developed a fusion rule that enhances the discriminatory information in the images. Here, a novel palm representation, called “Laplacianpalm” feature, is extracted from the fused images by the locality preserving projections (LPP). Unlike the Eigenpalm approach, the “Laplacianpalm” finds an embedding that preserves local information and yields a palm space that best detects the essential manifold structure. We compare the proposed “Laplacianpalm” approach with the Fisherpalm and Eigenpalm methods on a large data set. Experimental results show that the proposed “Laplacianpalm” approach provides a better representation and achieves lower error rates in palm recognition. Furthermore, the proposed multimodal method outperforms any of its individual modality.     

Channel estimation based on neural network in space time block coded MIMO–OFDM system

In this study, we propose feed-forward multilayered perceptron (MLP) neural network trained with the Levenberg–Marquardt algorithm to estimate channel parameters in MIMO–OFDM systems. Bit error rate (BER) and mean square error (MSE) performances of least square (LS) and least mean square error (LMS) algorithms are also compared to our proposed neural network to evaluate the performances. Neural network channel estimator has got much better performance than LS and LMS algorithms. Furthermore it doesn?t need channel statistics and sending pilot tones, contrary to classical algorithms.     

Research and development of message service based on JMS

Message service has got more and more extensive applications among the applied procedures used in business enterprise, and the message service developed with the CORBA and RMI, etc. costs a great deal of time and energy to carry on the maintenance of the code. According to the effective demand of the message service, This paper studies on the technique of JMS, puts forward a new kind of message service model based on JMS, and discusses the implementation of online recruitment system based on this model. At the same time, it also puts forward a homologous mechanism which is used to deal with the dead message problem that usually appears in this system.     

Design and performance analysis of RAID based on OSD

Today＇s SAN architectures promise unmediated host access to storage. To keep this promise, however, several issues and opportunities raised by SANs must be addressed, including security, scalability and management. Object storage, such as introduced by the NASD work, is a means of addressing these issues and opportunities. RAID using Object-Based Storage Devices is described, about which some issues are discussed and then its performance by queuing model is investigated.     

Research and application of cross-test strategy based on component

The technique of Component-Based Development obtained great promotion in recent years. It not only gives profound influence to the technique innovation of software industry, but also brings huge benefit for many other domains. However, the application of fine test methods can simplify test operation, reduce test cost and improve software quality. This paper proposes a kind of cross-test based on component according to traditional software test technique, aiming at the problems produced by new characteristics of software component technique, and verifies a telecommunication system using this test method fmally.     

Thermodynamic optimization of the Mn–P and Fe–Mn–P systems

Thermodynamic modeling of the Mn–P and Fe–Mn–P systems in the full composition was carried out using the CALculation of PHAse Diagrams (CALPHAD) method based on the critical evaluation of all available phase equilibria and thermodynamic data. The liquid and solid solutions were described using the Modified Quasichemical Model and Compound Energy Formalism, respectively. The Gibbs energies of the binary stoichiometric iron and manganese phosphides were determined based on reliable experimental data. The ternary (Fe,Mn)₃P, (Fe,Mn)₂P and (Fe,Mn)P phosphides were modeled as solid solutions with mutual substitution between Fe and Mn atoms. The Gibbs energy of the liquid solution was predicted using the Toop interpolation technique with P as an asymmetric component, without any ternary parameters. The thermodynamic properties of P in the entire composition region and the liquidus of the ternary system were well reproduced. Based on the thermodynamic models with optimized parameters, unexplored phase diagrams and thermodynamic properties of the Fe–Mn–P system were predicted.     

Optimal breast cancer classification using Gauss–Newton representation based algorithm

Breast cancer is a decisive disease worldwide. It is one of the most widely spread cancer among women. As per the survey, one out of eight women in the world are at risk of breast cancer at some point of time in her life. One of the methods to reduce breast cancer mortality rate is timely detection and effective treatment. That is why, more accurate classification of a breast cancer tumor has become a challenging problem in the medical field. Many classification techniques are proposed in the literature. Today, expert systems and machine learning techniques are being extensively used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we have proposed a novel Gauss-Newton representation based algorithm (GNRBA) for breast cancer classification. It uses the sparse representation with training sample selection. Until now, sparse representation has been successfully applied in pattern recognition only. The proposed method introduces a novel Gauss-Newton based approach to find the optimal weights for the training samples for classification. In addition, it evaluates the sparsity in a computationally efficient way as compared to the conventional l₁-norm method. The effectiveness of the GNRBA is examined on the Wisconsin Breast Cancer Database (WBCD) and the Wisconsin Diagnosis Breast Cancer (WDBC) database from the UCI Machine Learning repository. Various performance measures like classification accuracy, sensitivity, specificity, confusion matrices, a statistical test and the area under the receiver operating characteristic (AUC) are reported to show the superiority of the proposed method as compared to classical models. The experimental results show that the proposed GNRBA could be a good alternative for breast cancer classification for clinical experts.