Yan Xiang

ORCID: 0009-0007-0607-0762
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About
Contact & Profiles
Research Areas
  • Image Processing Techniques and Applications
  • Digital Media Forensic Detection
  • Advanced Image Processing Techniques
  • Speech Recognition and Synthesis
  • Advanced Computational Techniques and Applications
  • Gait Recognition and Analysis
  • Colorectal Cancer Screening and Detection
  • Cell Image Analysis Techniques
  • Speech and Audio Processing
  • Anomaly Detection Techniques and Applications
  • Industrial Vision Systems and Defect Detection

Guangdong Polytechnic of Science and Technology
2024

Macau University of Science and Technology
2023

Stony Brook University
2021

Northwestern University
2021

Louisiana State University
2021

Vanderbilt University Medical Center
2021

California University of Pennsylvania
2021

Brigham and Women's Hospital
2021

The Ohio State University
2021

Harvard University
2021

The extraction of characteristic parameters is extremely important front-end the speech recognition system, accurate Mongolian have meaning to its technology development. This paper clarified Mel frequency cepstrum coefficients(MFCC) features and method at first, then it will treated with first-order second-order difference, after that, combining these get a new feature parameter vector. Analyze extract this eigenvector from signal by means MATLAB. Experimental results reveal that more is,...

10.4028/www.scientific.net/amr.542-543.833 article EN Advanced materials research 2012-06-01

The virtual Pathology Visions 2020 (PV20) meeting of the Digital Association (DPA), held on October 26-29, set new records in almost every way.In response to COVID-19 pandemic, we strategically changed original face-to-face Orlando, FL, USA, a meeting.Unlike any other conference, PV20 proved that power digital technology coupled with dedication and creativity DPA is unstoppable create an engaging educational milestone promoting advancement pathology (DP) artificial intelligence (AI).The...

10.4103/2153-3539.326643 article EN cc-by-nc-sa Journal of Pathology Informatics 2021-01-01

In granular materials processing, how to efficiently recognize and remove foreign bodies is very important. Training data including H,I mean I standard deviation of target material are distinguished be or not by training model, which established using FWOCSVM method, while taking into account the characteristics body detection. A way introduce weight value reflecting importance degree attribute with its square developed solve problem, that is, weights considered in OCSVM. The results show...

10.4028/www.scientific.net/amr.734-737.2983 article EN Advanced materials research 2013-08-16
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