Abstract: Systems and methods for predicting fatigue crack growth are provided. In one example embodiment, a method can include obtaining historical operational data associated with one or more rotatable structures of one or more machines, obtaining data indicative of fatigue crack size for the one or more rotatable structures, and constructing a machine-learned model correlating fatigue crack growth with operational data using a machine learning technique.

Abstract: Systems and methods for predicting fatigue crack growth are provided. In one example embodiment, a method can include obtaining historical operational data associated with one or more rotatable structures of one or more machines, obtaining data indicative of fatigue crack size for the one or more rotatable structures, and constructing a machine-learned model correlating fatigue crack growth with operational data using a machine learning technique.

Abstract: 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.

Abstract: A method and apparatus for identifying running vehicles in an area to be monitored using acoustic signature recognition. The apparatus includes an input sensor for capturing an acoustic waveform produced by a vehicle source, and a processing system. The waveform is digitized and divided into frames. Each frame is filtered into a plurality of gammatone filtered signals. At least one spectral feature vector is computed for each frame. The vectors are integrated across a plurality of frames to create a spectro-temporal representation of the vehicle waveform. In a training mode, values from the spectro-temporal representation are used as inputs to a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In an active mode, the synaptic weights and acoustic signatures are used as patterns in a supervised associative network to identify whether a vehicle is present in the area to be monitored. In response to a vehicle being present, the class of vehicle is identified.

Abstract: 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 {bi} (i=1, . . . , imax) of breach classes bi. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes bi, by choosing a breach class within the set {bi} that has a maximum likelihood.

Abstract: A compact, inexpensive, and reliable fence intrusion detection system may detect activity on a fence and determine the type of activity based on discrimination. The hardware may include a 3-axis accelerometer and a RISC microprocessor. The system may be equipped with a wireless device which enables the system to work remotely and communicate with a base station. An algorithm may detect activity vs. no-activity on the fence. The algorithm may thereafter recognize the type of the activity; such as whether it is due to rattling caused by strong wind or a breach such as a person climbing the fence. The recognition algorithm may be computationally inexpensive and therefore also may be embedded inside a local RISC microcontroller. The system has been tested on different fences and demonstrated an over 90% correct recognition rate.

Abstract: A method and apparatus for identifying running vehicles in an area to be monitored using acoustic signature recognition. The apparatus includes an input sensor for capturing an acoustic waveform produced by a vehicle source, and a processing system. The waveform is digitized and divided into frames. Each frame is filtered into a plurality of gammatone filtered signals. At least one spectral feature vector is computed for each frame. The vectors are integrated across a plurality of frames to create a spectro-temporal representation of the vehicle waveform. In a training mode, values from the spectro-temporal representation are used as inputs to a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In an active mode, the synaptic weights and acoustic signatures are used as patterns in a supervised associative network to identify whether a vehicle is present in the area to be monitored. In response to a vehicle being present, the class of vehicle is identified.

Abstract: 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 Dynamic Synapse Neural Network (DSNN) with good false recognition rates. The models may reject quadrupedal animal footsteps.

Abstract: 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.

Abstract: 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 {bi} (i=1, . . . , imax) of breach classes bi. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes bi, by choosing a breach class within the set {bi} that has a maximum likelihood.

Abstract: A method and apparatus for identifying running vehicles in an area to be monitored using acoustic signature recognition. The apparatus includes an input sensor for capturing an acoustic waveform produced by a vehicle source, and a processing system. The waveform is digitized and divided into frames. Each frame is filtered into a plurality of gammatone filtered signals. At least one spectral feature vector is computed for each frame. The vectors are integrated across a plurality of frames to create a spectro-temporal representation of the vehicle waveform. In a training mode, values from the spectro-temporal representation are used as inputs to a Nonlinear Hebbian learning function to extract acoustic signatures and synaptic weights. In an active mode, the synaptic weights and acoustic signatures are used as patterns in a supervised associative network to identify whether a vehicle is present in the area to be monitored. In response to a vehicle being present, the class of vehicle is identified.