Richard V Field, Jr

Title: Richard V Field, Jr: Innovator in Boundary Detection Technologies

Introduction

Richard V Field, Jr. is an accomplished inventor based in Albuquerque, NM (US). He has made significant contributions to the field of data analysis through his innovative patent on boundary detection methods. His work focuses on creating efficient and robust techniques for identifying boundaries in various datasets.

Latest Patents

Richard holds a patent titled "Efficient generalized boundary detection." This patent describes fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including one-dimensional sequences. The methods utilize three simple algorithms to approximate the information distance between adjacent sliding windows within a dataset. One algorithm generates an initial ordered list of subsequences, while another updates this list by dropping the first entry and appending the last entry, thus avoiding the need to recalculate entirely new lists with each iteration. Large values in the distance metric indicate potential boundary locations. To enhance accuracy, a smoothed z-score or wavelet-based algorithm can be employed to locate peaks in the distance metric, effectively identifying boundary locations. An adaptive version of this method incorporates a collection of window sizes and corresponding weighting functions, making it suitable for real datasets with complex and changing structures.

Career Highlights

Richard is currently employed at National Technology & Engineering Solutions of Sandia, LLC, where he continues to develop innovative solutions in technology and engineering. His expertise in boundary detection has positioned him as a valuable asset in his field.

Collaborations

Richard has collaborated with talented coworkers, including Christina Ting and Tu-Thach Quach, contributing to a dynamic and innovative work environment.

Conclusion

Richard V Field, Jr. exemplifies the spirit of innovation through his groundbreaking work in boundary detection technologies. His contributions have the potential to significantly impact data analysis across various applications.