scholarly journals An Approximate Adder With a Near-Normal Error Distribution: Design, Error Analysis and Practical Application

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4518-4530
Author(s):  
Padmanabhan Balasubramanian ◽  
Raunaq Nayar ◽  
Douglas L. Maskell ◽  
Nikos E. Mastorakis
Author(s):  
Hyejin Lee ◽  
Junsoo Lee ◽  
Kyungso Im

AbstractIn this paper, we suggest new cointegration tests that can become more powerful in the presence of non-normal errors. Non-normal errors will not pose a problem in usual cointegration tests even when they are ignored. However, we show that they can become useful sources to improve the power of the tests when we use the “residual augmented least squares” (RALS) procedure to make use of nonlinear moment conditions driven by non-normal errors. The suggested testing procedure is easy to implement and it does not require any non-linear estimation techniques. We can exploit the information on the non-normal error distribution that is already available but ignored in the usual cointegration tests. Our simulation results show significant power gains over existing cointegration tests in the presence of non-normal errors.


2014 ◽  
Vol 945-949 ◽  
pp. 2183-2186
Author(s):  
Yun Xia Zhang ◽  
Ling Lan ◽  
Xiao Hui Wang

Based on measurement error of observation nodes is commom in mechanical system fault detection, but the traditional denoising method has many shortcomings. This paper introduce the Gibbs sampling method, which can be used to denoise and eliminate measurement error for node discreted information. We discuss it, and expect some promotion in practical application.


Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 966
Author(s):  
Fukang Yin ◽  
Jianping Wu ◽  
Junqiang Song ◽  
Jinhui Yang

In this paper, we proposed a high accurate and stable Legendre transform algorithm, which can reduce the potential instability for a very high order at a very small increase in the computational time. The error analysis of interpolative decomposition for Legendre transform is presented. By employing block partitioning of the Legendre-Vandermonde matrix and butterfly algorithm, a new Legendre transform algorithm with computational complexity O(Nlog2N /loglogN) in theory and O(Nlog3N) in practical application is obtained. Numerical results are provided to demonstrate the efficiency and numerical stability of the new algorithm.


Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 59 ◽  
Author(s):  
Xin-Wen Wang ◽  
Shi-Qing Tang ◽  
Yan Liu ◽  
Ji-Bing Yuan

In the practical application of quantum entanglement, entangled particles usually need to be distributed to many distant parties or stored in different quantum memories. In these processes, entangled particles unavoidably interact with their surrounding environments, respectively. We here systematically investigate the entanglement-decay laws of cat-like states under independent Pauli noises with unbalanced probability distribution of three kinds of errors. We show that the robustness of cat-like entangled states is not only related to the overall noise strength and error distribution parameters, but also to the basis of qubits. Moreover, we find that whether a multi-qubit state is more robust in the computational basis or transversal basis depends on the initial entanglement and number of qubits of the state as well as the overall noise strength and error distribution parameters of the environment. However, which qubit basis is conductive to enhancing the robustness of two-qubit states is only dependent on the error distribution parameters. These results imply that one could improve the intrinsic robustness of entangled states by simply transforming the qubit basis at the right moment. This robustness-improving method does not introduce extra particles and works in a deterministic manner.


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