Abstract: An autonomous vehicle and a system and method for operating the autonomous vehicle. The system includes a control system and a cognitive system. The control system performs a driving action at the autonomous vehicle. The cognitive system generates the driving action using an evaluation model. The evaluation model is generated by operating the cognitive system in response to a training set of data to generate a planned action for operating the autonomous vehicle by the cognitive system, evaluating the planned action to obtain a system performance grade, and updating the cognitive system based on a comparison of the system performance grade to a human-based performance grade.

Abstract: A computer-implemented method and system for modifying a musculoskeletal model is provided. Test data for a set of ergonomic tests performed by a number of test subjects fitting a profile is obtained. A number of muscle parameters for a number of muscles in the musculoskeletal model are adjusted using the test data to modify the musculoskeletal model to adapt to the profile.

Abstract: An autonomous vehicle and a system and method for operating the autonomous vehicle. The system includes a control system and a cognitive system. The control system performs a driving action at the autonomous vehicle. The cognitive system generates the driving action using an evaluation model. The evaluation model is generated by operating the cognitive system in response to a training set of data to generate a planned action for operating the autonomous vehicle by the cognitive system, evaluating the planned action to obtain a system performance grade, and updating the cognitive system based on a comparison of the system performance grade to a human-based performance grade.

Abstract: An autonomous vehicle and a system and method of operating the autonomous vehicle. A maneuver classifier is trained at an offline processor to identify a driving maneuver for a driving context. An online processor is configured to receive the driving context, operate the maneuver classifier to identify the driving maneuver based on the driving context, perform the driving maneuver at the autonomous vehicle, grade the driving maneuver as it is being performed at the autonomous vehicle, and adjust a performance of the driving maneuver at the autonomous vehicle based on the grade.

Abstract: Described is a system for health assessment. The system is implemented on a mobile device having at least one of an accelerometer, a geographic location sensor, and a camera. In operation, the system obtains sensor data related to an operator of the mobile device from one of the sensors. A network of networks (NoN) is generated based on the sensor data, the NoN having a plurality of layers with linked nodes. Tuples are thereafter generated. Each tuple contains a node from each layer that optimizes importance, diversity, and coherence. Storylines are created based on the tuples that solves a longest path problem for each tuple. The storylines track multiple symptom progressions of the operator. Finally, a disease prediction of the operator is provided based on the storylines.

Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

Abstract: Described is a system for selecting measurement nodes in a distributed physical system of agents. In operation, the distributed physical system is represented as a multi-layer network having a communication layer and an agent layer. The communication layer represents the amount of collective communication activities between any pair of areas and the agent layer represents movement of agents within the distributed physical system such that the communication layer and agent layer collectively generate network dynamics. The network dynamics are modeled as hybrid partial differential equations (PDEs) with measurable interconnected states in the communication layer. Notably, placement of a minimum set of measurement nodes is determined within the distributed physical system to provide full-state observability of the distributed physical system. The system can then track the full system state and apply compensation to one or more agents in the distributed physical system based on tracking the full system state.

Abstract: A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; an input spiking neural network configured to encode the features as a first plurality of spiking signals; a multi-layer recurrent neural network configured to compute a second plurality of spiking signals based on the first plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the second plurality of spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.

Abstract: Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.

Abstract: Described is a system for co-adaptation of robot control to human biomechanics. During operation, the system receives joint angle and joint velocity of a human and co-robot and generates estimated internal states of the human. A task space motion plan is then generated for the co-robot based on a specified cooperative task and estimated internal states and joint angle and joint velocity of the human. Joint torque commands are then generated based on the task space motion plan and joint angle and joint velocity of the human and co-robot. Motion of the co-robot is then controlled, such as causing the co-robot to actuate one or more actuators to move based on the joint torque commands.

Abstract: Described is neuromorphic system for authorized user detection. The system includes a client device comprising a plurality of sensor types providing streaming sensor data and one or more processors. The one or more processors include an input processing component and an output processing component. A neuromorphic electronic component is embedded in or on the client device for continuously monitoring the streaming sensor data and generating out-spikes based on the streaming sensor data. Further, the output processing component classifies the streaming sensor data based on the out-spikes to detect an anomalous signal and classify the anomalous signal.

Abstract: An autonomous vehicle, cognitive system for operating an autonomous vehicle and method of operating an autonomous vehicle. The cognitive system includes one or more hypothesizer modules, a hypothesis resolver, one or more decider modules, and a decision resolver. Data related to an agent is received at the cognitive system. The one or more hypothesizer modules create a plurality of hypotheses for a trajectory of the agent based on the received data. The hypothesis resolver selects a single hypothesis for the trajectory of the agent from the plurality of hypotheses based on a selection criteria. The one or more decider modules create a plurality of decisions for a trajectory of the autonomous vehicle based on the selected hypothesis for the agent. The decision resolver selects a trajectory for the autonomous vehicle from the plurality of decisions. The autonomous vehicle is operated based on the selected trajectory.

Abstract: Described is a system for prediction and active compensation of occupant motor response in a vehicle accident. The system uses a spinal reflex model to generate a stimulus based on an accident scenario of an occupant in a vehicle, the stimulus being a set of proprioceptive signals induced by the accident scenario. A neuromuscular model then determines activation and contraction dynamics based on the stimulus. The activation and contraction dynamics represent muscle contraction forces spanning a skeletal system of the occupant. A musculoskeletal model then generates a predicted motor response of the occupant based on the activation and contraction dynamics. The predicted motor response can be used for a variety of purposes, such as initiating active compensation in a vehicle or modifying airline cabin design parameters to decrease the likelihood of injury to the occupant.

Abstract: Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

Abstract: Described is a system for explaining how the human brain represents conceptual knowledge. A semantic model is developed, and a behavioral exam is performed to assess a calibration subject into a cohort and reveal semantic relationships to modify a personalized semantic space developed by the semantic model. Semantic features are extracted from the personalized semantic space. Neural features are extracted from neuroimaging of the human subject. A neuroceptual lattice is created having nodes representing attributes by aligning the semantic features and the neural features. Structures in the neuroceptual lattice are identified to quantify an extent to which the set of neural features represents a target concept. The identified structures are used to interpret conceptual knowledge in the brain of a test subject.

Abstract: Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

Abstract: Described is a system for online characterization of biomechanical and cognitive factors relevant to physical rehabilitation and training efforts. A biosensing subsystem senses biomechanical states of a user based on the output of sensors and generates a set of biomechanical data. The set of biomechanical data is transmitted in real-time to an analytics subsystem. The set of biomechanical data is analyzed by the analytics subsystem, and control guidance is sent through a real-time control interface to adjust the user's motions. In one aspect control guidance is sent to a robotic exoskeleton worn by the user to adjust the user's motions.

Abstract: Described is a system for decoding recorded signals into movement commands of a prosthetic device. Using a biomechanical model and physical action data, biological signal data is related to kinetic data. The physical action data can include position, joint angle, speed, and acceleration of a part of a limb. The biological signal data can include recorded neural signals and recorded muscle signals. The kinetic data can include force, power, torque, and stress. Based on the relationship between the biological signal data and the kinetic data, control commands are generated to achieve an intended position and/or movement of a prosthesis.

Abstract: An automotive vehicle includes an actuator configured to control vehicle acceleration, steering, or braking. The vehicle additionally includes at least one sensor configured to detect an object in the vicinity of the vehicle. The vehicle further includes at least one controller configured to automatically control the actuator according to an automated driving system algorithm. The at least one controller is configured to define a first vehicle route, detect at least one object in the vicinity of the vehicle; calculate a probabilistic forecast of a future location of the at least one detected object, define a second vehicle route in response to the probabilistic forecast indicating proximity of the at least one detected object to the first route, and to control the actuator to execute the second vehicle route.

Abstract: Described is neuromorphic system for authorized user detection. The system includes a client device comprising a plurality of sensor types providing streaming sensor data and one or more processors. The one or more processors include an input processing component and an output processing component. A neuromorphic electronic component is embedded in or on the client device for continuously monitoring the streaming sensor data and generating out-spikes based on the streaming sensor data. Further, the output processing component classifies the streaming sensor data based on the out-spikes to detect an anomalous signal and classify the anomalous signal.