Hyung Jin Park, 58606 Sunnybrook Dr, Brentwood, TN 37027

8077036, Dec 13, 2011

Oct 2, 2008

12/244549

Theodore W. Berger - Rancho Palos Verdes CA, US
Alireza Dibazar - Los Angeles CA, US
Ali Yousefi - Los Angeles CA, US
Hyung O. Park - Torrance CA, US

University of Southern California - Los Angeles CA

G08B 13/00

340566, 340565, 340544, 340522, 340943, 356486, 356502

A system for detecting and classifying a security breach may include at least one sensor configured to detect seismic vibration from a source, and to generate an output signal that represents the detected seismic vibration. The system may further include a controller that is configured to extract a feature vector from the output signal of the sensor and to measure one or more likelihoods of the extracted feature vector relative to set {b} (i=1,. . . , i) of breach classes b. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes b, by choosing a breach class within the set {b} that has a maximum likelihood.

8615476, Dec 24, 2013

Apr 13, 2010

12/759556

Theodore W. Berger - Rancho Palos Verdes CA, US
Alireza Dibazar - Los Angeles CA, US
Hyung O. Park - Torrance CA, US

University of Southern California - Los Angeles CA

G06F 15/18

706 12

An approaching human threat or vehicle, such as a suicide bomber nearing a secured zone such as a military base, may be detected and classified. A vibration recognition system may detect a systematic vibration event. The entity might be a medium, human, animal, or a passenger vehicle. The system may discriminate between such an event and a background or other vibration event, such as a falling tree limb. A seismic sensor may be employed to detect vibrations generated by footsteps and a vehicle. Seismic waves may be processed locally where the sensor is located. The system may wirelessly communicate with a remote command center. Temporal features of the vibration signals may be modeled by a biologically realistic neural network with good false recognition rates. The models may reject quadrupedal animal footsteps.

2010026, Oct 14, 2010

Apr 8, 2010

12/756657

Theodore W. Berger - Rancho Palos Verdes CA, US
Alireza Dibazar - Los Angeles CA, US
Hyung O. Park - Torrance CA, US

UNIVERSITY OF SOUTHERN CALIFORNIA - Los Angeles CA

G01V 1/30

367 38

Systems, methods, and apparatus are described that provide for analysis of seismic data. Features of temporal gait patterns can be extracted from seismic/vibration data. A mean temporal gait pattern can be determined. A statistical classifier can be used to model features of the data. The model can be used to classify the data. As a result, discrimination of seismic sources can be performed. Systems for discrimination of seismic data are also described. A system can include a vibration sensor system configured and arranged to detect vibrations. A system can also include a processor system configured and arranged to receive data from the vibration sensor, recognize the seismic data as belonging to a particular class of seismic data, and produce an output signal corresponding to the recognized particular class of seismic data.