A novel polygon-based circuit extraction algorithm for full custom designed MEMS sensors

2016 ◽  
Author(s):  
Axel Hald ◽  
Johannes Seelhorst ◽  
Mathias Reimann ◽  
Jurgen Scheible ◽  
Jens Lienig
Keyword(s):  
Mems Sensors ◽  
Circuit Extraction ◽  
Extraction Algorithm

scholarly journals There and back again: A circuit extraction tale

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 421 ◽  
Author(s):  
Miriam Backens ◽  
Hector Miller-Bakewell ◽  
Giovanni de Felice ◽  
Leo Lobski ◽  
John van de Wetering
Keyword(s):  
Quantum Circuit ◽  
Circuit Model ◽  
Bloch Sphere ◽  
Circuit Extraction ◽  
Extraction Algorithm ◽  
Rewrite Rules ◽  
Quantum Computations

Translations between the quantum circuit model and the measurement-based one-way model are useful for verification and optimisation of quantum computations. They make crucial use of a property known as gflow. While gflow is defined for one-way computations allowing measurements in three different planes of the Bloch sphere, most research so far has focused on computations containing only measurements in the XY-plane. Here, we give the first circuit-extraction algorithm to work for one-way computations containing measurements in all three planes and having gflow. The algorithm is efficient and the resulting circuits do not contain ancillae. One-way computations are represented using the ZX-calculus, hence the algorithm also represents the most general known procedure for extracting circuits from ZX-diagrams. In developing this algorithm, we generalise several concepts and results previously known for computations containing only XY-plane measurements. We bring together several known rewrite rules for measurement patterns and formalise them in a unified notation using the ZX-calculus. These rules are used to simplify measurement patterns by reducing the number of qubits while preserving both the semantics and the existence of gflow. The results can be applied to circuit optimisation by translating circuits to patterns and back again.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

Seismic monitoring network based on MEMS sensors

2019 ◽  
Vol 32 (3-4) ◽  
pp. 179-185
Author(s):  
Zhen-xuan Zou ◽  
◽  
Ming Zhang ◽  
Xu-dong He ◽  
Sheng-fa Lin ◽  
...  
Keyword(s):  
Monitoring Network ◽  
Seismic Monitoring ◽  
Mems Sensors

An Answer Extraction Algorithm Based on Syntax Structure Feature Parsing and Classification

2009 ◽  
Vol 31 (4) ◽  
pp. 662-676 ◽  
Author(s):  
Bao-Shun HU ◽  
Da-Ling WANG ◽  
Ge YU ◽  
Ting MA
Keyword(s):  
Structure Feature ◽  
Answer Extraction ◽  
Extraction Algorithm

Text extraction algorithm based on binary clustering

2009 ◽  
Vol 29 (1) ◽  
pp. 57-59
Author(s):  
Wei DAI ◽  
Shen-sheng ZHANG
Keyword(s):  
Text Extraction ◽  
Extraction Algorithm
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