Consensus Under Bounded Noise in Discrete Network Systems: An Algorithm With Fast Convergence and High Accuracy

2016 ◽  
Vol 46 (12) ◽  
pp. 2874-2884 ◽  
Author(s):  
Jianping He ◽  
Mengjie Zhou ◽  
Peng Cheng ◽  
Ling Shi ◽  
Jiming Chen
Keyword(s):  
High Accuracy ◽  
Fast Convergence ◽  
Bounded Noise ◽  
Network Systems

Adaptive Intensity Transformation based Phase-Retrieval with High Accuracy and Fast Convergence

2021 ◽  
Author(s):  
Songnian Fu ◽  
Meng Xiang ◽  
Peijian Zhou ◽  
Ye Bolin ◽  
Ou Xu ◽  
...  
Keyword(s):  
Phase Retrieval ◽  
High Accuracy ◽  
Fast Convergence

A note on fractional Bessel equation and its asymptotics

2013 ◽  
Vol 16 (3) ◽  
Author(s):  
Wojciech Okrasiński ◽  
Łukasz Płociniczak
Keyword(s):  
Power Series ◽  
Asymptotic Formula ◽  
Asymptotic Analysis ◽  
Fractional Derivative ◽  
Order Term ◽  
High Accuracy ◽  
Fast Convergence ◽  
Leading Order ◽  
Bessel Equation ◽  
Integer Order

AbstractIn this note we propose a fractional generalization of the classical modified Bessel equation. Instead of the integer-order derivatives we use the Riemann-Liouville version. Next, we solve the fractional modified Bessel equation in terms of the power series and provide an asymptotic analysis of its solution for large arguments. We find a leading-order term of the asymptotic formula for the solution to the considered equation. This behavior is verified numerically and shows high accuracy and fast convergence. Our results reduce to the classical formulas when the order of the fractional derivative goes to integer values.

Managing high-accuracy and fast convergence in OPC

2006 ◽  
Author(s):  
Klaus Herold ◽  
Norman Chen ◽  
Ian P. Stobert
Keyword(s):  
High Accuracy ◽  
Fast Convergence

A new method for cable shape finding of self-anchored suspension bridges

2019 ◽  
Author(s):  
Chao Liu ◽  
Zhan Gao
Keyword(s):  
High Accuracy ◽  
Measured Data ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Hebei Province ◽  
New Method ◽  
Suspension Bridges ◽  
Coordinate Method

<p>This paper proposes a new overall coordinate method (OCM) to determine the cable shape of self-anchored suspension bridges. In this method, the initial cable shape between adjacent clamps is assumed to be linear and the target cable shape is calculated by iterations based on the overall equilibrium of forces. This method is used to calculate the cable shape of the Chishui Bay Bridge in Hebei Province by MATLAB, and the results are compared with the measured data. The comparison shows that the OCM has a fast convergence speed and high accuracy.</p>

scholarly journals Entropy-Driven Adaptive Filtering for High-Accuracy and Resource-Efficient FPGA-Based Neural Network Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1765
Author(s):  
Elim Yi Lam Kwan ◽  
Jose Nunez-Yanez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Filtering ◽  
Real Data ◽  
High Accuracy ◽  
Frame Rate ◽  
Network Systems ◽  
Classification Rate ◽  
Rate System ◽  
Processing Rate

Binarized neural networks are well suited for FPGA accelerators since their fine-grained architecture allows the creation of custom operators to support low-precision arithmetic operations, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. Although good progress has been made to improve the accuracy of binarized networks, it can be significantly lower than networks where weights and activations have multi-bit precision. In this paper, we address this issue by adaptively choosing the number of frames used during inference, exploiting the high frame rates that binarized neural networks can achieve. We present a novel entropy-based adaptive filtering technique that improves accuracy by varying the system’s processing rate based on the entropy present in the neural network output. We focus on using real data captured with a standard camera rather than using standard datasets that do not realistically represent the artifacts in video stream content. The overall design has been prototyped on the Avnet Zedboard, which achieved 70.4% accuracy with a full processing pipeline from video capture to final classification output, which is 1.9 times better compared to the base static frame rate system. The main feature of the system is that while the classification rate averages a constant 30 fps, the real processing rate is dynamic and varies between 30 and 142 fps, adapting to the complexity of the data. The dynamic processing rate results in better efficiency that simply working at full frame rate while delivering high accuracy.

A New 1,000 kV Electron Microscope

1967 ◽  
Vol 25 ◽  
pp. 260-261
Author(s):  
M. Nishigaki ◽  
S. Katagiri ◽  
H. Kimura ◽  
B. Tadano
Keyword(s):  
Electron Microscope ◽  
High Voltage ◽  
Basic Research ◽  
Chromatic Aberration ◽  
High Accuracy ◽  
Biological Specimen ◽  
Voltage Electron ◽  
High Voltage Electron Microscope ◽  
High Voltage Electron

The high voltage electron microscope has many advantageous features in comparison with the ordinary electron microscope. They are a higher penetrating efficiency of the electron, low chromatic aberration, high accuracy of the selected area diffraction and so on. Thus, the high voltage electron microscope becomes an indispensable instrument for the metallurgical, polymer and biological specimen studies. The application of the instrument involves today not only basic research but routine survey in the various fields. Particularly for the latter purpose, the performance, maintenance and reliability of the microscope should be same as those of commercial ones. The authors completed a 500 kV electron microscope in 1964 and a 1,000 kV one in 1966 taking these points into consideration. The construction of our 1,000 kV electron microscope is described below.

Review on Fog Based Spectrum Sensing for Artificial Intelligence

2018 ◽  
pp. 66-70
Author(s):  
A. Rethina Palin ◽  
I. Jeena Jacob
Keyword(s):  
Bandwidth Allocation ◽  
Situational Awareness ◽  
Cluster Head ◽  
Mesh Network ◽  
Network Systems ◽  
Markov Logic ◽  
Wireless Mesh ◽  
Local Environments ◽  
Reactive Routing ◽  
The Hierarchical Structure

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

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