Bing Lu

Title: Innovations by Inventor Bing Lu

Introduction

Bing Lu is an accomplished inventor based in Los Angeles, CA. He has made significant contributions to the field of vehicle detection and classification through his innovative patents. With a total of 2 patents, his work focuses on utilizing acoustic signature recognition to identify running vehicles in monitored areas.

Latest Patents

Bing Lu's latest patents include "Detection and classification of running vehicles based on acoustic signatures" and "Acoustic signature recognition of running vehicles using spectro-temporal dynamic neural network." Both patents describe a method and apparatus for identifying running vehicles using acoustic signature recognition. The apparatus captures an acoustic waveform produced by a vehicle, which is then digitized and divided into frames. Each frame undergoes filtering into multiple gammatone filtered signals, and spectral feature vectors are computed for each frame. These vectors are integrated across frames to create a spectro-temporal representation of the vehicle waveform. In training mode, values from this representation are used in a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In active mode, these weights and signatures are utilized in a supervised associative network to determine the presence and class of vehicles in the monitored area. Results are then relayed to a central computer.

Career Highlights

Bing Lu is affiliated with the University of Southern California, where he continues to advance his research and innovations. His work has garnered attention for its potential applications in enhancing vehicle monitoring systems.

Collaborations

Bing Lu collaborates with notable colleagues, including Theodore W. Berger and Alircza Dibazar, contributing to a dynamic research environment that fosters innovation.

Conclusion

Bing Lu's contributions to vehicle detection technology through his patents demonstrate his expertise and commitment to innovation. His work not only enhances vehicle monitoring systems but also showcases the potential of acoustic signature recognition in various applications.