Study on optimization of Sampling Function for reduction of TEM Noise

2017 ◽  
Author(s):  
Wu Xin ◽  
Xiam Pan ◽  
Fang Guang-You ◽  
Rao Li-Ting ◽  
Guo Rui
Keyword(s):  
Sampling Function

scholarly journals Locating stationary paths in functional integrals: An optimization method utilizing the stationary phase Monte Carlo sampling function

1989 ◽  
Vol 90 (6) ◽  
pp. 3181-3191 ◽  
Author(s):  
Thomas L. Beck ◽  
J. D. Doll ◽  
David L. Freeman
Keyword(s):  
Monte Carlo ◽  
Stationary Phase ◽  
Optimization Method ◽  
Monte Carlo Sampling ◽  
Functional Integrals ◽  
Sampling Function

A maximum entropy approach to sampling function design

1988 ◽  
Vol 4 (3) ◽  
pp. 829-841 ◽  
Author(s):  
S P Luttrell
Keyword(s):  
Maximum Entropy ◽  
Sampling Function ◽  
Function Design

scholarly journals Lattice-Based Linearly Homomorphic Signature Scheme over F 2

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jie Cai ◽  
Han Jiang ◽  
Hao Wang ◽  
Qiuliang Xu
Keyword(s):  
Public Key ◽  
Side Channel ◽  
Signature Scheme ◽  
Side Channel Attacks ◽  
Sampling Function ◽  
Uniform Sampling ◽  
Restricted Condition ◽  
Gaussian Sampling

In this paper, we design a new lattice-based linearly homomorphic signature scheme over F 2 . The existing schemes are all constructed based on hash-and-sign lattice-based signature framework, where the implementation of preimage sampling function is Gaussian sampling, and the use of trapdoor basis needs a larger dimension m ≥ 5 n   log   q . Hence, they cannot resist potential side-channel attacks and have larger sizes of public key and signature. Under Fiat–Shamir with aborting signature framework and general SIS problem restricted condition m ≥ n   log   q , we use uniform sampling of filtering technology to design the scheme, and then, our scheme has a smaller public key size and signature size than the existing schemes and it can resist side-channel attacks.

scholarly journals ID-Based Strong Designated Verifier Signature over R-SIS Assumption

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Jie Cai ◽  
Han Jiang ◽  
Pingyuan Zhang ◽  
Zhihua Zheng ◽  
Hao Wang ◽  
...  
Keyword(s):  
Random Model ◽  
Side Channel ◽  
Complex Structures ◽  
Rejection Sampling ◽  
Side Channel Attacks ◽  
Sampling Function ◽  
Uniform Sampling ◽  
Image Sampling ◽  
Designated Verifier ◽  
Designated Verifier Signature

In this paper, we propose an ID-based strong designated verifier signature (SDVS) over R-SIS assumption in the random model. We remove pre-image sampling function and Bonsai trees such complex structures used in previous lattice-based SDVS schemes. We only utilize simple rejection sampling to protect the security of our scheme. Hence, we will show our design has the shortest signature size comparing with existing lattice-based ID-based SDVS schemes. In addition, our scheme satisfies anonymity (privacy of signer’s identity) proved in existing schemes rarely, and it can resist side-channel attacks with uniform sampling.

A narrowband multi-ary FSK transmission principle using sampling function waveform modulation

2002 ◽  
Author(s):  
N. Kuroyanagi ◽  
N. Suehiro ◽  
K. Ohtake ◽  
M. Tomita ◽  
L. Guo
Keyword(s):  
Sampling Function

3124 Sequential Approximate Optimization Using RBF Network : Proposal of the Sampling Function

2007 ◽  
Vol 2007.6 (0) ◽  
pp. 307-308
Author(s):  
Jun OHHAMA ◽  
Satoshi KITAYAMA ◽  
Koetsu YAMAZAKI
Keyword(s):  
Sequential Approximate Optimization ◽  
Sampling Function ◽  
Rbf Network ◽  
Approximate Optimization

scholarly journals Fabrication of a Micro-Fluid Gathering Tool for the Gastrointestinal Juice Sampling Function of a Versatile Capsular Endoscope

Sensors ◽  
2011 ◽  
Vol 11 (7) ◽  
pp. 6978-6990 ◽  
Author(s):  
Kyo-in Koo ◽  
Sangmin Lee ◽  
Dong-il Dan Cho
Keyword(s):  
Sampling Function

QUANTUM MONTE CARLO SIMULATIONS OF FERMIONS: A MATHEMATICAL ANALYSIS OF THE FIXED-NODE APPROXIMATION

2006 ◽  
Vol 16 (09) ◽  
pp. 1403-1440 ◽  
Author(s):  
ERIC CANCÈS ◽  
BENJAMIN JOURDAIN ◽  
TONY LELIÈVRE
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Mathematical Analysis ◽  
Ground State ◽  
Quantum Monte Carlo ◽  
Stochastic Methods ◽  
Sampling Function ◽  
Long Time ◽  
Nodal Surfaces ◽  
Fixed Node

The Diffusion Monte Carlo (DMC) method is a powerful strategy to estimate the ground state energy E0 of an N-body Schrödinger Hamiltonian H = -½Δ + V with high accuracy. It consists of writing E0 as the long-time limit of an expectation value of a drift-diffusion process with a source term, and numerically simulating this process by means of a collection of random walkers. As for a number of stochastic methods, a DMC calculation makes use of an importance sampling function ψI which hopefully approximates some ground state ψ0 of H. In the fermionic case, it has been observed that the DMC method is biased, except in the special case when the nodal surfaces of ψI coincide with those of a ground state of H. The approximation due to the fact that, in practice, the nodal surfaces of ψI differ from those of the ground states of H, is referred to as the Fixed Node Approximation (FNA). Our purpose in this paper is to provide a mathematical analysis of the FNA. We prove that, under convenient hypotheses, a DMC calculation performed with the importance sampling function ψI, provides an estimation of the infimum of the energy 〈ψ, Hψ〉 on the set of the fermionic test functions ψ that exactly vanish on the nodal surfaces of ψI.

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