Sieving Algorithm Research Articles

A Real-Time Monitoring System for Cable Tension with Vibration Signals Based on an Automated Algorithm to Sieve Out Reliable Modal Frequencies

Cables or suspenders are the critical force-transmitting components of cable-supported bridges, and their timely tension monitoring is consequently the most important issue in the corresponding structural health monitoring. However, very few works regarding the full automation of vibration-based tension estimation have been reported in the literature. To develop a monitoring system of cable tension based on real-time vibration signals, this research first employs an efficient stochastic subspace identification (SSI) method with tailored parameter selection to continuously identify the three frequencies of adjacent modes for the cables of Mao-Luo-Hsi Bridge. More importantly, an automated sieving algorithm is delicately established to obtain the stable modal frequencies by making the best of the specific modal frequency distribution for cables. The ratios between the frequency values identified from SSI analysis are exhaustively checked to systematically extract the qualified cable frequencies and decide their corresponding mode orders. The tension is finally computed with one available cable frequency according to the priority order predetermined by the statistics of identification rate. Demonstrated by analyzing the vibration signals measured from the stay cable of Mao-Luo-Hsi Bridge in real time for two full years, the effectiveness and robustness of this real-time monitoring system have been extensively testified. The long-term success rates for the immediate determination of dependable tension are found to be perfect for 15 of the 18 investigated cables. As for the other three cables, their corresponding success rates are still higher than 99.99% with very few cases of absent or false tension values.

Solving HNP with One Bit Leakage: An Asymmetric Lattice Sieving Algorithm

The Hidden Number Problem (HNP) was introduced by Boneh and Venkastesan to analyze the bit-security of the Diffie-Hellman key exchange scheme. It is often used to mount a side-channel attack on (EC)DSA. The hardness of HNP is mainly determined by the number of nonce leakage bits and the size of the modulus. With the development of lattice reduction algorithms and lattice sieving, the range of practically vulnerable parameters are extended further. However, 1-bit leakage is still believed to be challenging for lattice attacks. In this paper, we proposed an asymmetric lattice sieving algorithm that can solve HNP with 1-bit leakage. The algorithm is composed of a BKZ pre-processing and a sieving step. The novel part of our lattice sieving algorithm is that the lattice used in these two steps have different dimensions. In particular, in the BKZ step we use more samples to derive a better lattice basis, while we just use truncated lattice basis for the lattice sieving step. To verify our algorithm, we use it to solve HNP with 1-bit leakage and 116-bit modulus.

Parallelization on Gauss Sieve Algorithm over Ideal Lattice

Cryptanalysis of lattice-based cryptography is an important field in cryptography since lattice problems are among the most robust assumptions and have been used to construct a variety of cryptographic primitives. The security estimation model for concrete parameters is one of the most important topics in lattice-based cryptography. In this research, we focus on the Gauss Sieve algorithm proposed by Micciancio and Voulgaris, a heuristic lattice sieving algorithm for the central lattice problem, shortest vector problem (SVP).We propose a technique of lifting computations in prime-cyclotomic ideals into that in cyclic ideals. Lifting makes rotations easier to compute and reduces the complexity of inner products from O(n^3) to O(n^2). We implemented the Gauss Sieve on multi-GPU systems using two layers of parallelism in our framework, and achieved up to 55 times speed of previous results of dimension 96. We were able to solve SVP on ideal lattice in dimension up to 130, which is the highest dimension SVP instance solved by sieve algorithm so far. As a result, we are able to provide a better estimate of the complexity of solving central lattice problem.

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems.

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

An improved sieve point method for the reliability analysis of structures

The efficiency of existing stochastic analysis method depends on the discretization of the random variables domain. The number theoretical method has been proposed to discretize the random variable space and solve the generalized density evolution equation via sampling strategy. This method traditionally involves hyper-ball sieving (HS) algorithm to sample the representative point set. However, the sieving radius of the hyper-ball is determined subjectively, and the efficiency and accuracy of the analysis depend on the selected radius. To avoid this subjective selection, an equal volume hyper-ball sieving method is presented in this paper. By transforming the hypercube spatial volume of random variables into that of an equivalent hyper-ball, the radius of the equal volume hyper-ball is obtained analytically. This radius is further optimized with a minimum star discrepancy in the representative point set. The performance and accuracy of the proposed method are checked in four numerical examples, and the representative point set such obtained is more uniform with smaller NRP leading to more accurate and efficient subsequent stochastic analysis than the HS method.

Improvement of the quadratic sieve method on the basis of the extended factor base and using available quantity of B – smooth numbers

In information and telecommunication systems, RSA algorithms are often used to solve information security problems. At the core of the cryptostability of the most popular today asymmetric cryptographic algorithm RSA is the complexity of the factorization of large integers. Quadratic sieve method is the best for factorization of integers under 110 decimal digits or so. The most time consuming part of the algorithm of a quadratic sieve is the sieving process. The size of the factor base is one of the key parameters that determine the effectiveness of the sieving algorithm. Too large factor base requires the search for a large number of B–smooth numbers, which increases the total execution time of the algorithm. When the size is less than necessary, it will not be possible to find a sufficient number of B–smooth numbers. In this paper, the method for determining and applying a sufficient size of B–smooth numbers with doubling the factor base size in comparison with the basic algorithm of a quadratic sieve is proposed. With the expansion of the factor base, the number of N numbers increases, which can be decomposed into factors by the quadratic sieve method. It is also noted that its increase leads to an increase in computational complexity, since it is advisable to find a greater number of B–smooth numbers. However, when conducting numerical experiments, where the size of the factor base increased twice, it turned out that using the proposed algorithm, the time necessary to find a sufficient number of B-smooth numbers, on the contrary, decreased.

Online Detection of Particle Agglomeration during Solution Crystallization by Microscopic Double-View Image Analysis

To detect the particle agglomeration degree for assessing crystal growth quality during a crystallization process, an in situ image analysis method is proposed based on a microscopic double-view imaging system. First, a fast image preprocessing approach is adopted for segmenting raw images taken simultaneously from two cameras installed at different angles, to reduce the influence from uneven illumination background and solution turbulence. By defining an index of the inner distance based curvature for different particle shapes, a preliminary sieving algorithm is then used to identify candidate agglomerates. By introducing two texture descriptors for pattern recognition, a feature matching algorithm is subsequently developed to recognize pseudoagglomerates in each pair of the double-view images. Finally, a fast algorithm is proposed to count the number of recognized particles in these agglomerates, besides the unagglomerated particles. Experimental results from the potassium dihydrogen phosphate (KDP) cry...

A sieve algorithm based on overlattices

AbstractIn this paper, we present a heuristic algorithm for solving exact, as well as approximate, shortest vector and closest vector problems on lattices. The algorithm can be seen as a modified sieving algorithm for which the vectors of the intermediate sets lie in overlattices or translated cosets of overlattices. The key idea is hence no longer to work with a single lattice but to move the problems around in a tower of related lattices. We initiate the algorithm by sampling very short vectors in an overlattice of the original lattice that admits a quasi-orthonormal basis and hence an efficient enumeration of vectors of bounded norm. Taking sums of vectors in the sample, we construct short vectors in the next lattice. Finally, we obtain solution vector(s) in the initial lattice as a sum of vectors of an overlattice. The complexity analysis relies on the Gaussian heuristic. This heuristic is backed by experiments in low and high dimensions that closely reflect these estimates when solving hard lattice problems in the average case.This new approach allows us to solve not only shortest vector problems, but also closest vector problems, in lattices of dimension$\def \xmlpi #1{}\def \mathsfbi #1{\boldsymbol {\mathsf {#1}}}\let \le =\leqslant \let \leq =\leqslant \let \ge =\geqslant \let \geq =\geqslant \def \Pr {\mathit {Pr}}\def \Fr {\mathit {Fr}}\def \Rey {\mathit {Re}}n$in time$2^{0.3774\, n}$using memory$2^{0.2925\, n}$. Moreover, the algorithm is straightforward to parallelize on most computer architectures.

On the importance of cache tuning in a cache-aware algorithm: A case study

In the present paper we describe and analyse a sieving algorithm for determining prime numbers. This external memory algorithm contains several parameters which are related to the sizes of the levels in the memory hierarchy. We examine how we should choose the values of these parameters in order to obtain an optimal running time. We compare the running times obtained by varying the parameters. We conclude that in this specific problem fine tuning pays off as we got a speed-up of almost 40%.

Discussion: Vehicle classification from weigh-in-motion data: the progressive sieving algorithm

Discussion: Vehicle classification from weigh-in-motion data: the progressive sieving algorithm

Reply: Vehicle classification from weigh-in-motion data: the progressive sieving algorithm

A classification procedure has been developed, based on the concept of progressive sieving of weigh-in-motion data through a series of criteria designed to separate, identify, and classify vehicles by their axle arrangement. The procedure is demonstrated to be feasible, and is capable of identifying all major types of vehicles quite accurately. The procedure together with a nomenclature provides a classification system that is open-ended having an infinite number of classes. The system is universal and could be used by any jurisdiction. It facilitates evaluation of vehicle weight and dimension policy alternatives and their impacts on the highway system. The procedure has the capability of translating data from one classification system into another.(A)

Vehicle classification from weigh-in-motion data: the progressive sieving algorithm

A classification procedure has been developed, based on the concept of progressive sieving of weigh-in-motion data through a series of criteria designed to separate, identify, and classify vehicles by their axle arrangement. The procedure is demonstrated to be feasible, and is capable of identifying all major types of vehicles quite accurately. The procedure together with a nomenclature provides a classification system that is open-ended having an infinite number of classes. The system is universal and could be used by any jurisdiction. It facilitates evaluation of vehicle weight and dimension policy alternatives and their impacts on the highway system. The procedure has the capability of translating data from one classification system into another. Key words: classification, sieving, axle spacing, vehicle configuration.