Abstract: Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.

Abstract: Hidden Markov Models (“HMMs”) are used to analyze keystroke dynamics measurements collected as a user types a predetermined string on a keyboard. A user enrolls by typing the predetermined string several times; the enrollment samples are used to train a HMM to identify the user. A candidate who claims to be the user provides a typing sample, and the HMM produces a probability to estimate the likelihood that the candidate is the user he claims to be. A computationally-efficient method for preparing HMMs to analyze certain types of processes is also described.

Abstract: Hidden Markov Models (“HMMs”) are used to analyze keystroke dynamics measurements collected as a user types a predetermined string on a keyboard. A user enrolls by typing the predetermined string several times; the enrollment samples are used to train a HMM to identify the user. A candidate who claims to be the user provides a typing sample, and the HMM produces a probability to estimate the likelihood that the candidate is the user he claims to be. A computationally-efficient method for preparing HMMs to analyze certain types of processes is also described.

Abstract: We develop a system consisting of a neural architecture resulting in classifying regions corresponding to users' keystroke patterns. We extend the adaptation properties to classification phase resulting in learning of changes over time. Classification results on login attempts of 43 users (216 valid, 657 impersonation samples) show considerable improvements over existing methods.

Abstract: An improved network of sensor nodes can self-organize so as to reduce energy consumption and/or improve coverage of a surveillance field. Nodes within the network may be dynamically activated or deactivated so as to lengthen network lifetime and/or enhance sensor coverage of the surveillance field.