Abstract: One embodiment provides a method and a system for reconstructing symbols transmitted over a high frequency (HF) communication channel. During operation, the system can receive, at a receiver, a radio frequency (RF) signal carrying an input data frame and transmitted over the HF communication channel. The input data frame includes a number of known symbols followed by a number of unknown symbols. The system can determine a set of channel parameters associated with the HF communication channel based on the received RF signal and the known symbols and reconstruct, using a machine-learning technique, the unknown symbols based on the determined channel parameters and the received RF signal.

Abstract: A transferable hybrid method for prognostics of engineering systems based on fundamental degradation modes is provided. The method includes developing a degradation model that represents degradation modes shared in different domains of application through the integration of physics and machine learning. The system measures sensor signals and data processing provides for extracting health indicators correlated with the fundamental degradation modes from sensors data. For the integration of physics and machine learning, the degradation mode is separated into different phases. Before the accelerated degradation phase of a system, the method is looking out to detect when the accelerated phase begins. When accelerated phase is active, the system applies a machine-learning model to provide information on the accelerated degradation phase, and evolves the degradation towards a failure threshold in a simulation of the updated physics-based model to predict the degradation progression.

Abstract: One embodiment provides a system which facilitates construction of an ensemble of neural network-based classifiers that optimize a diversity metric. During operation, the system defines a diversity metric based on pairwise angles between decision boundaries of three or more affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing a pair of mutually orthogonal affine classifiers of the three or more affine classifiers. The system trains one or more neural networks such that parameters of the one or more neural networks are consistent with parameters of the affine classifiers to obtain an ensemble of neural network-based classifiers which optimize the diversity metric. The system predicts an outcome for a testing data object based on the obtained ensemble of neural-network based classifiers which optimize the diversity metric.

Abstract: A system determines an input video and a first annotated image from the input video which identifies an object of interest. The system initiates a tracker based on the first annotated image and the input video. The tracker generates, based on the first annotated image and the input video, information including: a sliding window for false positives; a first set of unlabeled images from the input video; and at least two images with corresponding labeled states. A semi-supervised classifier classifies, based on the information, the first set of unlabeled images from the input video. If a first unlabeled image is classified as a false positive, the system reinitiates the tracker based on a second annotated image occurring in a frame prior to a frame with the false positive. The system generates an output video comprising the input video displayed with tracking on the object of interest.

Abstract: One embodiment provides a method and system which facilitates optimizing a pair of affine classifiers based on a diversity metric. During operation, the system defines a diversity metric based on an angle between decision boundaries of a pair of affine classifiers. The system includes the diversity metric as a regularization term in a loss function optimization for designing the pair of affine classifiers, wherein the designed pair of affine classifiers are mutually orthogonal. The system predicts an outcome for a testing data object based on the designed pair of mutually orthogonal affine classifiers.

Abstract: Embodiments described herein provide a design architecture for co-designing a controller and a watermarking signal for a cyber-physical system. During operation, the architecture can determine, in conjunction with each other, respective values of a first set of parameters indicating operations of the controller and a second set of parameters representing the watermarking signal. Here, the watermarking signal is combinable with a control signal from the controller for monitoring an output signal of the cyber-physical system for detecting malicious data at different time instances. Subsequently, the architecture can determine a state manager for determining the states of the cyber-physical system from the monitored output signal based on the first and second sets of parameters. The architecture can also determine a detector capable of identifying presence of an attack from the states of the cyber-physical system at a plurality of time instances using the watermarking signal.

Abstract: A system and method are provided wherein encrypted data is stored in modular lockers on a first storage volume associated with a high-power microprocessor deployed in a system. Each encrypted locker maps, for example, to a specific time segment (e.g., one day) which simplifies mounting of the encrypted volume for data access and reduces the locker size for external access of data while encrypted (e.g., via Wifi). New data in a second storage volume associated with and generated by a low-power microprocessor associated with the system gets transferred to the encrypted data store during wake cycles of the high-power microprocessor. To manage space on the first storage volume, time stamps associated with each encrypted locker allows simple removal of files older than a specified time period by removing of any encrypted lockers older than that threshold.

Abstract: A system and method provide a combination of a modular message structure, a priority-based message packing scheme, and a data packet queue management system to optimize the information content of a transmitted message in, for example, the Ocean of Things (OoT) environment. The modular message structure starts with a header that provides critical information and reference points for time and location. The rest of the message is composed of modular data packets, each of which has a data ID section that the message decoder uses for reference when reconstructing the message contents, an optional size section that specifies the length of the following data section if it can contain data of variable length, and a data section that can be compressed in a manner unique to that data type.

Abstract: A method and system for implementing a task scheduler are provided in a resource constrained computation system that uses meta data provided for each task (e.g. data analysis algorithm or sensor sampling protocol) to determine which tasks should be run in a particular wake cycle, the order in which the tasks are run, and how the tasks are distributed across the available compute resources. When a task successfully completes, it's time of execution is logged in order to provide a reference for when that task should be run again. Task meta data is formatted in a manner to allow for simple integration of new tasks into the processing architecture.

Abstract: A nondestructive method for detecting damage in parts and/or characterizing effective material properties may include: exposing a material to one or more nondestructive stimuli; measuring a response of the material to the stimuli; selecting at least one of a specific length scale or a specific time scale; and analyzing the measurement of the response with a scale-aware single- or multi-physics model to identify anomalies in the measurements as compared to an expected response of the material to the stimuli, wherein the scale-aware single- or multi-physics model is based on the at least one of the specific length scale or the specific time scale.

Abstract: Mass vaccination of one or more animals may be performed topically using viscosified fluids and a spray delivery device. When applied to one or more animals, the viscosified fluids may adhere a medicament upon a topical surface of the one or more animals. The spray delivery device may comprise at least a spray component, a fluid reservoir component, a plunger, an electronics component, and a battery.

Abstract: A method systematically developing reduced-order (e.g., upscaled or coarse-grained, lumped- or distributed-parameter) multi-physics models for simulating additive manufacturing may include: describing governing equations of an additive manufacturing process; refactoring the governing equations into (1) constitutive laws with unknown coefficients and (2) conservation laws; discretizing the governing equations; and training the unknown coefficients of the constitutive laws with simulated data and/or experimental data relating to the additive manufacturing process where the conservation laws are enforced in the training regardless of a granularity of the constitutive laws, thereby yielding a reduced-order set of governing equations.

Abstract: A system is provided which mixes static scene and live annotations for labeled dataset collection. A first recording device obtains a 3D mesh of a scene with physical objects. The first recording device marks, while in a first mode, first annotations for a physical object displayed in the 3D mesh. The system switches to a second mode. The system displays, on the first recording device while in the second mode, the 3D mesh including a first projection indicating a 2D bounding area corresponding to the marked first annotations. The first recording device marks, while in the second mode, second annotations for the physical object or another physical object displayed in the 3D mesh. The system switches to the first mode. The first recording device displays, while in the first mode, the 3D mesh including a second projection indicating a 2D bounding area corresponding to the marked second annotations.

Abstract: A system and method provide extractions of regions of interest from images hand annotated by reviewers by lifting annotations from images, filtering out text labels, reconstructing continuous closed boundaries, and marking the contained region.

Abstract: A method and system are provided for an improved semantic segmentation using a multi-stream late fusion using pretrained encoders to encode disparate channels independently while also integrating selected image features at a more abstract level in order to provide improved localization and image classification.

Abstract: One embodiment provides a method and a system for computing diagnoses for a physical system. During operation, the system can obtain a design of the physical system, generate a design of a diagnostic system by augmenting the design of the physical system based on a number of fault-emulating subsystems, and convert the design of the diagnostic system into a polynomial formula comprising a plurality of variables. The plurality of variables can include inputs and outputs of the original physical system and a number of ancillary variables. The system can further embed the polynomial formula on a hardware-based solver configured to perform optimization using the polynomial formula as an objective function to obtain a diagnostic vector used for explaining faults in the physical system.

Abstract: A method of producing hydrogen may include: providing a catalyst-carbon gel; and flowing a hydrocarbon through a catalyst, wherein catalyst is a metal or mixture of metals that is non-wetting to solid carbon at 1 bar absolute and 10° C. above a melting point of the metal or mixture of metals.

Abstract: A system is provided which obtains images of a physical object captured by an AR recording device in a 3D scene. The system measures a level of diversity of the obtained images, for a respective image, based on at least: a distance and angle; a lighting condition; and a percentage of occlusion. The system generates, based on the level of diversity, a first visualization of additional images to be captured by projecting, on a display of the recording device, first instructions for capturing the additional images using the AR recording device. The system trains a model based on the collected data. The system performs an error analysis on the collected data to estimate an error rate for each image of the collected data. The system generates, based on the error analysis, a second visualization of further images to be captured. The model is further trained based on the collected data.

Abstract: Embodiments described herein provide a supervisor for fault management at a production system. During operation, the supervisor can obtain a set of sensor readings and a state of the production system. A respective sensor reading is an output of a sensor in the production system. The supervisor can then determine, using an artificial intelligence (AI) model, whether the set of sensor readings accommodates a fault associated with a corresponding sensor. Subsequently, the supervisor can determine an action that mitigates an effect of the fault and modify the set of sensor readings based on the action. Here, the modified set of sensor readings is used by a controller that controls the production system.

Abstract: A self-destructing device includes a frangible substrate having at least one pre-weakened area. A heater is thermally coupled to the frangible substrate proximate to or at the pre-weakened area. When activated, the heater generates heat sufficient to initiate self-destruction of the frangible substrate by fractures that propagate from the pre-weakened area and cause the frangible substrate to break into many pieces.