Public Key Cryptosystems Based on Drinfeld Modules Are Insecure

We show that analogues of popular public key cryptosystems based on Drinfeld modules are insecure by providing polynomial time algorithms to solve the Drinfeld module versions of the inversion and discrete logarithm problems. Received August 1999 and revised October 2000 Online publication 9 April 2001     

基于DCT的三重水印算法

根据HVS的特性和DCT系数的特点,提出了一种基于DC系数和AC系数的自适应三重水印嵌入方案。仿真实验表明：该算法具有良好的不可见性,并且在抵抗剪切操作以及噪声、滤波和JPEG压缩等常见的信号处理,表现出较强的鲁棒性。     

Determination of the mechanical properties of maize grains and olives required for use in DEM simulations

The discrete element method (DEM) is a numerical technique specifically designed for the simulation of the mechanical behavior of granular materials (e.g., granular food or pharmaceutical products). To make full use of the potential of this technique, values relating to certain microscopic properties of the material under study must be known, yet the literature contains little information in this respect. Indeed, some of the values required are difficult to obtain experimentally, and there are no standardized procedures in place for their determination. The present work reports the experimental determination of values for several of the microscopic properties – the particle density, modulus of elasticity, particle–wall coefficient of restitution, particle–particle coefficient of restitution, and the particle–wall coefficient of friction – of maize grains and olives, required for use in DEM simulations. To make preliminary evaluation of the assay procedures, glass beads, a better known material, was also used. The apparatuses and experimental protocols employed are explained, the difficulties arising with each described, and the results obtained discussed.     

高等数学教学中学生创新能力的培养

从激发学生学习高等数学的兴趣、培养大学生数学思维能力以及运用新的数学教学方式三个方面论高等数学教学中大学生创新能力的培养。     

Numerical analysis and testing of a stable and convergent finite element scheme for approximate deconvolution turbulence models

This report presents a stable and convergent finite element scheme for the approximate deconvolution turbulence models (ADM). The ADM is a popular turbulence model intensely studied lately but the computation of its numerical solution raises issues in terms of efficiency and accuracy. This report addresses this question. The proposed scheme presented herein is based on a new interpretation of the ADM model recently introduced by the author. Following this interpretation, the solution of the ADM is viewed as the average of a perturbed Navier–Stokes system. The scheme uses the Crank–Nicolson time discretization and the finite element spatial discretization and is proved to be stable and convergent provided a moderate choice of the time step is made. Numerical tests to verify the convergence rates and performance on a benchmark problem are also provided and they prove the correctness of this approach to numerically solve the ADM.     

A geometric model for an effective rescheduling after reducing service in public transportation systems

Railway systems in metropolitan areas carry a high density of traffic daily, heterogeneously distributed, and exposed to the negative consequences derived from service disruptions. An interesting topic in the literature is to obtain performance protocols in the presence of contingencies which can affect the system operation, avoiding the propagation of perturbation and minimizing its consequences. The objective of this paper is to assess the decision of rescheduling the train service, reducing the current supply along one transportation line in order to reinforce the service of another line, exploited by the same public operator, which has suffered an incidence or emergency. A methodology, based on a geometric representation of solutions which allows the use of discrete optimization techniques, is developed in order to attend to the underlying demand with efficiency criteria in this context of unexpected incidents.     

Effective compressed sensing MRI reconstruction via hybrid GSGWO algorithm

In a bio-imaging context, the main issues which obstruct the CS (Compressed sensing) application are image reconstruction time and computational cost. This paper presents an effective compressed sensing-based MRI reconstruction through a hybrid optimization algorithm. Initially, the preprocessing stage is performed using Cross guided bilateral filter. Then the K-space is generated by the Fourier transform. The hybrid Walsh Hadamard Transform and Discrete Wavelet Transform (HWHDWT) is utilized for the compressive sensing of the images. Finally, the Hybrid Galactic Swarm Optimization and Grey Wolf Optimization (HGSGWO) algorithm are developed for MRI reconstruction. The dataset collected from a hospital which contains MRI images both in JPEG and DICOM format. The performance of SSIM (Structural Similarity Index), PSNR (Peak Signal to Noise Ratio), MSE (mean square error) and reconstruction time are evaluated for images and it is compared with the existing methods.     

Investigation of cryogenic level sensors for LN2 and LOX

Investigations of two different types of cryogenic level sensors (capacitance and High Temperature Superconductor (HTS) for level measurement of liquid nitrogen (LN₂) and liquid oxygen (LOX) are presented here. They were tested for an active length of 400 mm in LOX and LN₂. A discrete diode array level sensor was used as a primary standard for calibrating these sensors. Comparative studies on linearity, sensitivity and other parameters at the operating temperatures are presented.     

Development of a discrete roughness index for longitudinal road profiles

Engineers of off-road equipment, on-road vehicles, pavement and tyres must assess the roughness of a terrain surface for the design of their products. A ubiquitous roughness index is the International Roughness Index (IRI), which quantifies the roughness of a section of road based on the average suspension travel for a particular vehicle at a prescribed speed. The Discrete Roughness Index (DRI) developed in this work addresses a fundamental limitation of the IRI. Specifically, the DRI is calculated for each discretely measured location along a terrain surface; furthermore, the DRI is applicable to vehicles travelling at varying speeds and parameters other than the Golden Quarter-Car on which the IRI is based. The development begins with a consistent discretisation of the terrain surface, vehicle response and the IRI. The Fractional Response Coefficient is then developed which serves a fundamental role in the development of the DRI. Finally, the DRI is developed and its properties are discussed through theory and simulation of the ASTM E1926-08 Profile. One important property of the average DRI is that it converges to the IRI as the distance between sampled points becomes smaller, for the particular case when the Golden Quarter-Car model is simulated at 80 kph. The DRI is a more general solution of the roughness estimation problem than the IRI; therefore, the IRI is a proper subset of the DRI in which the DRI is averaged over a section of road.     

A transform-based fast fuzzy C-means approach for high brain MRI segmentation accuracy

Segmentation of brain magnetic resonance (MR) images has a significant impact on the computer-aided diagnosis and analysis. However, due to the presence of noise in medical images, many segmentation methods suffer from limited segmentation accuracy. To reduce the effect of noise and achieve high segmentation accuracy many approaches based on the local and nonlocal information in the spatial domain have been proposed in the past. Recently, the authors have proposed a discrete cosine transform (DCT)-based local and nonlocal fuzzy C-means method (DCT-LNLFCM) which performs much better than the existing methods. However, the method is slow in speed. This paper presents a fast DCT-based nonlocal fuzzy C-means (DCT-NLFCM) segmentation method which is not only very fast than the DCT-LNLFCM, but also provides better segmentation results. The proposed method uses DCT-based MR pre-filtered image to achieve high segmentation accuracy and its histogram enables to achieve very high computation speed. Detailed experiments are conducted to establish the superiority of the proposed method over the state-of-the-art unsupervised methods.