Analysis of surface error correction capability of 1.2m active support system

2016 ◽  
Author(s):  
Yu Han ◽  
Bin Fan ◽  
Chaoqiang Li ◽  
Haitao Liu
Keyword(s):  
Error Correction ◽  
Support System ◽  
Surface Error ◽  
Error Correction Capability

Tooth surface error correction of hypoid gears machined by duplex helical method

2021 ◽  
Author(s):  
Shun-xing Wu ◽  
Hong-zhi Yan ◽  
Zhi-yong Wang ◽  
Ren-gui Bi ◽  
Zhi Chen ◽  
...  
Keyword(s):  
Error Correction ◽  
Tooth Surface ◽  
Surface Error ◽  
Hypoid Gears

A partial transmit sequence technique with error correction capability and low computation

2013 ◽  
Vol 27 (12) ◽  
pp. 4014-4027 ◽  
Author(s):  
Hsin-Ying Liang ◽  
Hung-Chi Chu ◽  
Chuan-Bi Lin ◽  
Kuang-Hao Lin
Keyword(s):  
Error Correction ◽  
Partial Transmit Sequence ◽  
Error Correction Capability

scholarly journals Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction

2019 ◽  
Vol 9 (21) ◽  
pp. 4670 ◽  
Author(s):  
Tao Liu ◽  
Bin Yan ◽  
Jeng-Shyang Pan
Keyword(s):  
Error Correction ◽  
Secret Sharing ◽  
Quick Response ◽  
Visual Secret Sharing ◽  
Response Code ◽  
Bar Code ◽  
Error Correction Capability ◽  
Quick Response Code ◽  
Transaction Security ◽  
Processing Steps

Visual secret sharing is a secret sharing scheme where the decryption requires no computation. It has found many applications in online transaction security, privacy protection, and bar code security, etc. Recently, researches have indicated that combining visual secret sharing with the widely used Quick Response code may provide additional security mechanism to online transaction. However, current methods are either pixel-based, which requires high computational complexity or module-based, which sacrifices error correction capability of the original Quick Response code. Designing module-based visual secret sharing for the Quick Response code without sacrificing error correction capability is a challenging problem. To solve this problem, this paper proposes a (3, 3)-threshold visual secret sharing for Quick Response code scheme that fully explores the extra freedom provided by color visual secret sharing and color stacking. The binary secret Quick Response code is encoded into color shares. By stacking all the three shares, a binary color Quick Response code can be reconstructed. After the inherent pre-processing steps in a standard Quick Response code decoder, the original binary secret Quick Response code can be completely reconstructed. Thus, the original error correction capability of the Quick Response code is fully preserved. Theoretical analysis shows that the visual secret sharing for Quick Response code is secure under the condition that the computational device available to the attacker is limited to a decoder for standard Quick Response code. Experimental results verify that the secret Quick Response code cannot be reconstructed from just one share or any two shares. However, it can be 100% reconstructed once the three shares are stacked. The proposed visual secret sharing for Quick Response code is module-based, and it does not sacrifice the error correction capability. Furthermore, No extra pre-processing steps other than the standard Quick Response code decoder are required.

On the Characteristics of the Autoassociative Memory with Nonzero-Diagonal Terms in the Memory Matrix

1991 ◽  
Vol 3 (3) ◽  
pp. 428-439 ◽  
Author(s):  
Jung-Hua Wang ◽  
Thomas F. Krile ◽  
John F. Walkup ◽  
Tai-Lang Jong
Keyword(s):  
Error Correction ◽  
Learning Algorithm ◽  
Attraction Force ◽  
Stable States ◽  
Outer Product ◽  
First Order ◽  
Error Correction Capability ◽  
Error Ratio ◽  
Successful Recall ◽  
Input Error

A statistical method is applied to explore the unique characteristics of a certain class of neural network autoassociative memory with N neurons and first-order synaptic interconnections. The memory matrix is constructed to store M = αN vectors based on the outer-product learning algorithm. We theoretically prove that, by setting all the diagonal terms of the memory matrix to be M and letting the input error ratio ρ = 0, the probability of successful recall Pr steadily decreases as α increases, but as α increases past 1.0, Pr begins to increase slowly. When 0 < ρ ≤ 0.5, the network exhibits strong error-correction capability if α ≤ 0.15 and this capability is shown to rapidly decrease as α increases. The network essentially loses all its error-correction capability at α = 2, regardless of the value of ρ. When 0 < ρ ≤ 0.5, and under the constraint of Pr > 0.99, the tradeoff between the number of stable states and their attraction force is analyzed and the memory capacity is shown to be 0.15N at best.

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