Real-time efficiency of Ada in a multiprocessor environment

1987 ◽  
Author(s):  
Anders Ardo
Keyword(s):  
Real Time ◽  
Time Efficiency

scholarly journals Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1167
Author(s):  
Ruber Hernández-García ◽  
Ricardo J. Barrientos ◽  
Cristofher Rojas ◽  
Wladimir E. Soto-Silva ◽  
Marco Mora ◽  
...  
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Individual Identification ◽  
Affine Transformations ◽  
Private Companies ◽  
Time Efficiency ◽  
Finger Vein ◽  
Vein Recognition ◽  
Coarse To Fine ◽  
Finger Vein Recognition

Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

Real-Time Interpolator and Contour Tracking in Link-Space for a 3 DOF Parallel Mechanism

2008 ◽  
Vol 594 ◽  
pp. 415-436
Author(s):  
Yuan Ming Cheng ◽  
Chien Hsun Kuo ◽  
Jih Hua Chin
Keyword(s):  
Real Time ◽  
Trajectory Tracking ◽  
Inverse Kinematics ◽  
Parallel Mechanism ◽  
Contour Tracking ◽  
Control Points ◽  
Parallel Mechanisms ◽  
Time Efficiency ◽  
Compensation Control ◽  
Space Approach

Parallel mechanisms could be hardly used in contour tracking because of their mechanism features. This study proposed a link-space real time contour tracking for a 3 DOF (Z、α and β) hydraulic parallel mechanism. The essence of this approach is to convert control points of command trajectory to link space by inverse kinematics. A real-time interpolator was created and the multi-axis cross-coupled pre-compensation control (MCCPM) was constructed for link-space contour tracking. It was shown that a contour-accurate trajectory tracking could be performed which was impossible in the original Z-α-β space. Other advantages of this link-space approach were time efficiency and the uniform tracking velocity.

A Deep Lifelong Learning Method for Digital-Twin Driven Defect Recognition With Novel Classes

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Gao Yiping ◽  
Li Xinyu ◽  
Liang Gao
Keyword(s):  
Lifelong Learning ◽  
Real Time ◽  
Learning Strategy ◽  
Development Trend ◽  
Smart Manufacturing ◽  
Learning Method ◽  
Time Data ◽  
Time Efficiency ◽  
Digital Twins ◽  
Defect Recognition

Abstract Recently, digital twins (DTs) have become a research hotspot in smart manufacturing, and using DTs to assist defect recognition has also become a development trend. Real-time data collection is one of the advantages of DTs, and it can help the realization of real-time defect recognition. However, DT-driven defect recognition cannot be realized unless some bottlenecks of the recognition models, such as the time efficiency, have been solved. To improve the time efficiency, novel defect class recognition is an essential problem. Most of the existing methods can only recognize the known defect classes, which are available during training. For new incoming classes, known as novel classes, these models must be rebuilt, which is time-consuming and costly. This greatly impedes the realization of DT-driven defect recognition. To overcome this problem, this paper proposes a deep lifelong learning method for novel class recognition. The proposed method uses a two-level deep learning architecture to detect and recognize novel classes, and uses a lifelong learning strategy, weight imprinting, to upgrade the model. With these improvements, the proposed method can handle novel classes timely. The experimental results indicate that the proposed method achieves good results for the novel classes, and it has almost no delay for production. Compared with the rebuilt methods, the time cost is reduced by at least 200 times. This result suggests that the proposed method has good potential in the realization of DT-driven defect recognition.

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