Abstract: A method for providing stimulation of living tissue may include generating electrical pulses onto multiple output channels to a plurality of electrodes each corresponding to one of the multiple output channels. The method may also include disconnecting the plurality of electrodes from recording amplifiers during a stimulation. Additionally, the method may include shorting, after each stimulation before recording is resumed, inputs of the recording amplifiers to ground to suppress ringing in the recording system.

Abstract: A method for estimating output neuron spikes based on input-output spike data that includes input spike data and corresponding output spike data includes formulating, using a controller, a weight value that characterizes plastic properties of a neuron based on the input-output spike data. The method further includes receiving, by the controller, an input spike train. The method further includes estimating, using the controller, an output spike train that corresponds to the input spike train by applying the weight value to a feedforward kernel that estimates the output spike train based on the input spike train and the weight value.

Abstract: A method for providing stimulation of living tissue may include generating electrical pulses onto multiple output channels to a plurality of electrodes each corresponding to one of the multiple output channels. The method may also include disconnecting the plurality of electrodes from recording amplifiers during a stimulation. Additionally, the method may include shorting, after each stimulation before recording is resumed, inputs of the recording amplifiers to ground to suppress ringing in the recording system.

Abstract: A method for estimating output neuron spikes based on input-output spike data that includes input spike data and corresponding output spike data includes formulating, using a controller, a weight value that characterizes plastic properties of a neuron based on the input-output spike data. The method further includes receiving, by the controller, an input spike train. The method further includes estimating, using the controller, an output spike train that corresponds to the input spike train by applying the weight value to a feedforward kernel that estimates the output spike train based on the input spike train and the weight value.

Abstract: Systems and techniques, including machine-readable instructions, for modeling of nonlinear systems. In one aspect, an apparatus includes a collection of two or more inputs configured and arranged to receive input signals, a collection of two or more outputs configured and arranged to output output signals, a processing unit configured to transform the input signals into the output signals, wherein the transformation is non-linear and treats the non-linear system as a collection of multiple input, single output non-linear systems, and a data storage that stores characteristics of the transformation.

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: Systems and techniques, including machine-readable instructions, for modeling of nonlinear systems. In one aspect, an apparatus includes a collection of two or more inputs configured and arranged to receive input signals, a collection of two or more outputs configured and arranged to output output signals, a processing unit configured to transform the input signals into the output signals, wherein the transformation is non-linear and treats the non-linear system as a collection of multiple input, single output non-linear systems, and a data storage that stores characteristics of the transformation.

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 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.

Abstract: Systems and techniques, including machine-readable instructions, for modeling of nonlinear systems. In one aspect, an apparatus includes a collection of two or more inputs configured and arranged to receive input signals, a collection of two or more outputs configured and arranged to output output signals, a processing unit configured to transform the input signals into the output signals, wherein the transformation is non-linear and treats the non-linear system as a collection of multiple input, single output non-linear systems, and a data storage that stores characteristics of the transformation.

Abstract: An acoustic event location and classification system comprising an array of at least two acoustic transducers arranged spaced from one another; a central data processing unit for receiving signals from the acoustic transducers and processing the signals to determine a event type and location; and an internet or LAN connection for transmitting event type and location data to a third party, wherein the central data processing unit uses a DSNN to determine the event type and generalized cross correlation functions between microphone pairs to determine the event location.

Abstract: An information processing system having signal processors that are interconnected by processing junctions that simulate and extend biological neural networks. Each processing junction receives signals from one signal processor and generates a new signal to another signal processor. The response of each processing junction is determined by internal junction processes and is continuously changed with temporal variation in the received signal. Different processing junctions connected to receive a common signal from a signal processor respond differently to produce different signals to downstream signal processors. This transforms a temporal pattern of a signal train of spikes into a spatio-temporal pattern of junction events and provides an exponential computational power to signal processors. Each signal processing junction can receive a feedback signal from a downstream signal processor so that an internal junction process can be adjusted to learn certain characteristics embedded in received signals.

Abstract: An information processing system having signal processors that are interconnected by processing junctions that simulate and extend biological neural networks. Each processing junction receives signals from one signal processor and generates a new signal to another signal processor. The response of each processing junction is determined by internal junction processes and is continuously changed with temporal variation in the received signal. Different processing junctions connected to receive a common signal from a signal processor respond differently to produce different signals to downstream signal processors. This transforms a temporal pattern of a signal train of spikes into a spatio-temporal pattern of junction events and provides an exponential computational power to signal processors. Each signal processing junction can receive a feedback signal from a downstream signal processor so that an internal junction process can be adjusted to learn certain characteristics embedded in received signals.