Chengdu, China

Fan Li

USPTO Granted Patents = 3 

Average Co-Inventor Count = 5.3

ph-index = 2

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2018-2020

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3 patents (USPTO):Explore Patents

Title: Innovations and Contributions of Inventor Fan Li

Introduction

Fan Li is a notable inventor based in Chengdu, China. He has made significant contributions to the fields of malware detection and medical imaging. With a total of 3 patents, his work showcases a blend of technical expertise and innovative thinking.

Latest Patents

One of Fan Li's latest patents is a "Method for detecting malware within a Linux platform." This method involves disassembling ELF format benign software and malware samples to generate assembly files. It traverses these files to analyze the code segments, identifying the presence of the main function and dividing the assembly code into basic blocks. The process establishes a control flow graph to classify ELF samples using a machine learning tool named Weka.

Another significant patent is the "X-ray chest image rib suppression method based on Poisson model." This method enhances X-ray images by conducting contourlet transformation and utilizing texture enhancement techniques. It employs a strip-type detection filter based on the Hessian matrix eigenvalue to identify rib locations. The Poisson model is then solved to suppress ribs effectively, improving the observation quality of X-ray chest images.

Career Highlights

Fan Li has worked with prominent institutions such as Sichuan University and Beijing Tongtech Co., Ltd. His experience in these organizations has contributed to his development as an inventor and researcher.

Collaborations

Throughout his career, Fan Li has collaborated with notable colleagues, including Junfeng Wang and Yulin Ji. These partnerships have fostered innovation and the exchange of ideas in their respective fields.

Conclusion

Fan Li's contributions to technology and medical imaging demonstrate his commitment to innovation. His patents reflect a deep understanding of complex systems and a drive to improve existing methodologies.

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