Analyzing radiation absorption difference of dental substance by using Dual CT

The purpose of this study was to evaluate the changes of noise and computer tomography (CT) number in each dental substance, by using the metal artefact reduction algorithm; we used dual CT for this study. For the study, we produced resin, titanium, gypsum, and wax that are widely used by dentists. In addition, we made nickel to increase the artefact. While making the study materials, we made sure that there is no difficulty when inserting the substances inside phantom. In order to study, we scanned before and after using the metal artefact reduction algorithm. We conducted an average analysis of CT number and noise, before and after using the metal artefact reduction algorithm. As a result, there was no difference in CT number and noise before and after using the metal artefact reduction algorithm. However, when it comes to the noise value in each substance, wax's noise value was the lowest whereas titanium's noise value was the highest, after applying the metal artefact reduction algorithm. In nickel, CT number and noise value from artefact area showed a decreased noise value when applying the metal artefact reduction algorithm. In conclusion, we assumed that we could increase the effectiveness of CT examination by applying dual energy's metal artefact reduction algorithm.     

Speeding up thermalisation via open quantum system variational optimisation

Optimising open quantum system evolution is an important step on the way to achieving quantum computing and quantum thermodynamic tasks. In this article, we approach optimisation via variational principles and derive an open quantum system variational algorithm explicitly for Lindblad evolution in Liouville space. As an example of such control over open system evolution, we control the thermalisation of a qubit attached to a thermal Lindbladian bath with a damping rate γ. Since thermalisation is an asymptotic process and the variational algorithm we consider is for fixed time, we present a way to discuss the potential speedup of thermalisation that can be expected from such variational algorithms.     

Nonadiabatic Quantum Search Algorithm with Analytical Success Rate

In nonadiabatic quantum search algorithm, it is difficult to calculate the success rate analytically. We develop the nonadiabatic quantum search algorithm by adding a counterdiabatic driving term to the original time-dependent Hamiltonian. The Hamiltonian we structured is diagonal in eigen picture and the time-independent Schrödinger equation is solved analytically. Then, we get an accurate analytical expression of success rate in nonadiabatic quantum search algorithm. Utilizing this expression, a sufficient condition, which can ensure the success rate be one with arbitrary evolution time, was found. Moreover, we can choose the better parameters by calculating the precise success rate according to the expression.

     

Detecting communities in clustered networks based on group action on set

In this paper, we propose a well targeted algorithm (GAS algorithm) for detecting communities in high clustered networks by presenting group action technology on community division. During the processing of this algorithm, the underlying community structure of a clustered network emerges simultaneously as the corresponding partition of orbits by the permutation groups acting on the node set are achieved. As the derivation of the orbit partition, an algebraic structure r-cycle can be considered as the origin of the community. To be a priori estimation for the community structure of the algorithm, the community separability is introduced to indicate whether a network has distinct community structure. By executing the algorithm on several typical networks and the LFR benchmark, it shows that this GAS algorithm can detect communities accurately and effectively in high clustered networks. Furthermore, we compare the GAS algorithm and the clique percolation algorithm on the LFR benchmark. It is shown that the GAS algorithm is more accurate at detecting non-overlapping communities in clustered networks. It is suggested that algebraic techniques can uncover fresh light on detecting communities in complex networks.     

Continued Fractions and the d-Dimensional Gauss Transformation

In this paper we study a multidimensional continued fraction algorithm which is related to the Modified Jacobi–Perron algorithm considered by Podsypanin and Schweiger. We demonstrate that this algorithm has many important properties which are natural generalisations of properties of one-dimensional continued fractions. For this reason, we call the transformation associated to the algorithm the d-dimensional Gauss transformation. We construct a coordinate system for the natural extension which reveals its symmetries and allows one to give an explicit formula for the density of its invariant measure. We also discuss the ergodic properties of this invariant measure. Received: 4 February 2000 / Accepted: 23 May 2000     

An Exact Quantum Search Algorithm with Arbitrary Database

In standard Grover’s algorithm for quantum searching, the probability of finding a marked state is not exactly 1, and some modified versions of Grover’s algorithm that search a marked state from an evenly distributed database with full successful rate have been presented. In this article, we present a generalized quantum search algorithm that searches M marked states from an arbitrary distributed N-item quantum database with a zero theoretical failure rate, where N is not necessary to be the power of 2. We analyze the general properties of our search algorithm, we find that our algorithm has periodicity with a period of 2J + 1, and it is effective with certainty for J + (2J + 1)m times of iteration, where m is an arbitrary nonnegative number.     

Segmentation algorithm for non-stationary compound Poisson processes

We introduce an algorithm for the segmentation of a class of regime switching processes. The segmentation algorithm is a non parametric statistical method able to identify the regimes (patches) of a time series. The process is composed of consecutive patches of variable length. In each patch the process is described by a stationary compound Poisson process, i.e. a Poisson process where each count is associated with a fluctuating signal. The parameters of the process are different in each patch and therefore the time series is non-stationary. Our method is a generalization of the algorithm introduced by Bernaola-Galván, et al. [Phys. Rev. Lett. 87, 168105 (2001)]. We show that the new algorithm outperforms the original one for regime switching models of compound Poisson processes. As an application we use the algorithm to segment the time series of the inventory of market members of the London Stock Exchange and we observe that our method finds almost three times more patches than the original one.     

Encoding entanglement-assisted quantum stabilizer codes

We address the problem of encoding entanglement-assisted (EA) quantum error-correcting codes (QECCs) and of the corresponding complexity. We present an iterative algorithm from which a quantum circuit composed of CNOT, H, and S gates can be derived directly with complexity O(n²) to encode the qubits being sent. Moreover, we derive the number of each gate consumed in our algorithm according to which we can design EA QECCs with low encoding complexity. Another advantage brought by our algorithm is the easiness and efficiency of programming on classical computers.     

Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.     

Minimal spanning tree problem in stock networks analysis: An efficient algorithm

Since the last decade, minimal spanning trees (MSTs) have become one of the main streams in econophysics to filter the important information contained, for example, in stock networks. The standard practice to find an MST is by using Kruskal’s algorithm. However, it becomes slower and slower when the number of stocks gets larger and larger. In this paper we propose an algorithm to find an MST which has considerably promising performance. It is significantly faster than Kruskal’s algorithm and far faster if there is only one unique MST in the network. Our approach is based on the combination of fuzzy relation theory and graph theoretical properties of the forest of all MSTs. A comparison study based on real data from four stock markets and four types of simulated data will be presented to illustrate the significant advantages of the proposed algorithm.     

Construction of Myazawa's Equations for the Case of an Arbitrary Number of Junction Atoms

In an earlier paper, by the technique of incomplete mathematical induction, the algorithm of construction of Miyazawa's equations for an arbitrary number of “junction atoms” in macromolecular chains were “guessed.” In this paper, we describe the technique of complete mathematical induction to prove the correctness of this algorithm.     

Mathematical Foundations of the Dimensional Method

We summarize and discuss important mathematical foundations concerning the dimensional method. To this end we carefully discuss the assumptions as well as the derived fundamental statements. We define and investigate the uniqueness of the ?_μνστ tensor in n dimensions and, related to this, the uniqueness of γ₅. Furthermore, we develop an algorithm which allows the calculation of a set of scalar multi-loop integrals with masses in a systematic way. To demonstrate and test the algorithm we rederive a set of scalar integrals which are important in calculations at the one and two loop level.     

A post-processing technique for extending depth of focus in conventional optical microscopy

In this paper, we propose a post-processing technique to obtain optical microscope images with extended depth of focus using a conventional microscope. With the proposed technique, we collect a sequence of images focused at different depths. We then combine the in-focus regions of each acquired frame to compose a single all-in-focus image. That is, a new image with extended depth of focus is obtained. The key to such an algorithm is in selecting the “in-focus” regions from each frame. In this paper, we describe the technique used to identify the in-focus region on every depth slice. Quantitative simulation results are presented where mean absolute error is used as a metric to assess the algorithm performance. Results using real imagery are also presented for subjective evaluation. Based on subjective evaluation and the quantitative simulation results, we believe that the proposed algorithm provides useful depth of focus extension.     

Finding jets and summing soft gluons: a new algorithm

We examine a new algorithm for finding jets ine ⁺ e ^– annihilation, using a jet measure based on relative transverse momentum. We perform an analytic calculation of the three-jet fraction at lowest order, and compare our result with the standard jet-finding algorithm. For soft gluons in an abelian theory it is shown that the leading double logarithms exponentiate, unlike the situation for the commonly used algorithm based on invariant mass. In QCD we find that there are leading non-abelian logarithms, and we calculate these explicitly atO( _s ² ). We discuss the modifications to the algorithm which are needed when the mass of a parton cluster is taken into account. The hope is that the new algorithm will allow an improved theoretical analysis at smaller values of the resolution parametery _T , and hence an improved fit to the experimental data.Nick Brown was tragically killed on 13th June, 1991. This paper is dedicated to his memory     

Automatized segmentation of photovoltaic modules in IR-images with extreme noise

Local electric defects may result in considerable performance losses in solar cells. Infrared thermography is an essential tool to detect these defects on photovoltaic modules. Accordingly, IR-thermography is frequently used in R&D labs of PV manufactures and, furthermore, outdoors in order to identify faulty modules in PV-power plants. Massive amount of data is acquired which needs to be analyzed. An automatized method for detecting solar modules in IR-images would enable a faster and automatized analysis of the data.However, IR-images tend to suffer from rather large noise, which makes an automatized segmentation challenging. The aim of this study was to establish a reliable segmentation algorithm for R&D labs. We propose an algorithm, which detects a solar cell or module within an IR-image with large noise. We tested the algorithm on images of 10 PV-samples characterized by highly sensitive dark lock-in thermography (DLIT). The algorithm proved to be very reliable in detecting correctly the solar module. In our study, we focused on thin film solar cells, however, a transfer of the algorithm to other cell types is straight forward.     

Quantum Image Encryption Algorithm Based on NASS

This paper proposes a quantum image encryption algorithm based on n-qubit normal arbitrary superposition state (NASS) by using the basic scheme of quantum transformation and random phase transformation. According to theoretical analysis and experimental simulation on MATLAB system, we find that key space is an important factor of encryption and decryption algorithm. When the secret key space is large, it is difficult for the attacker to crack the encrypted information. Based on this finding, we perform 2ⁿ +?4 times phase transformation in the encryption process. And each transformation is random, which increases the difficulty of decryption. So there are a total of 2ⁿ +?4 randomly transformed keys. In this paper, we design the implementation circuit of random phase transformation, and because the real quantum computer is not in our grasp, now we use MATLAB software to simulate grayscale image and color image encryption algorithm in classic computer, respectively. And the histogram, complexity and correlation are analyzed. Study shows that the proposed encryption algorithm is valid.     

Performance analysis and design of quasi-cyclic LDPC codes for underwater magnetic induction communications

Magnetic induction (MI) communication is an effective scheme for underwater wireless communication. In this paper, we aim to design an underwater MI communication system based on Quasi-cyclic LDPC (QC-LDPC) codes. Firstly, for a given QC-LDPC code used in underwater MI communication, we propose a novel algorithm to evaluate its performance, which is named as underwater magnetic induction protograph (UWMIP) extrinsic information transfer algorithm. Furthermore, we present a differential evolution UWMIP (DE-UWMIP) algorithm, which incorporates the differential evolution method and the UWMIP algorithm. By this algorithm, we search the optimized QC-LDPC codes with best distance threshold. Simulation results show that the proposed algorithm is effective and provide a good guidance to design the underwater MI communication system.     

From Nosé–Hoover chain to Nosé–Hoover network: design of non-Hamiltonian equations of motion for molecular-dynamics with multiple thermostats

A systematic algorithm to design multiple thermostat systems in the framework of the Nosé–Hoover type non-Hamiltonian formulation is presented. Using ‘non uniform’ time transformations in a generalised Hamiltonian equation, we develop the non-Hamiltonian equations of motion for multiple thermostat systems having an arbitrary number of thermostats and arbitrary connections between a physical system and thermostats (‘Nosé–Hoover network’). We then present the algorithm to construct the Nosé–Hoover network equations based on a simple diagram only. On the basis of this algorithm, recursively attached Nosé–Hoover thermostats are introduced as an example of the Nosé–Hoover network and its high efficiency in sampling the canonical distribution for an one-dimensional double-well system is illustrated by numerical calculations.