Abstract: A system and methods for monitoring and controlling a mental state of a subject are provided. In some aspects, a method includes receiving physiological and behavioral data acquired using the plurality of sensors while the subject is performing a task, and applying, using the data, a state-space framework to determine a plurality of decoder parameters correlating brain activity and behavior with a mental state of the subject. The method also includes identifying the mental state of the subject using the decoder parameters, and generating a report indicating the mental state of the subject. In some aspects, the method further includes generating, based on the identified mental state, a brain stimulation to treat a brain condition of the subject.

Abstract: A system and methods for monitoring and controlling a mental state of a subject are provided. In some aspects, a method includes receiving physiological and behavioral data acquired using the plurality of sensors while the subject is performing a task, and applying, using the data, a state-space framework to determine a plurality of decoder parameters correlating brain activity and behavior with a mental state of the subject. The method also includes identifying the mental state of the subject using the decoder parameters, and generating a report indicating the mental state of the subject. In some aspects, the method further includes generating, based on the identified mental state, a brain stimulation to treat a brain condition of the subject.

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 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.