Abstract: System and method that allow to jointly cause movement of multiple micro-and-nano-objects to desired positions are described. A high speed camera tracks the locations of the objects. An array of electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces. One or more computing devices are used to: process images captured by the camera to estimate positions of the objects; use model predictive control optimization to obtain trajectories and electrode potentials for moving at least some of the objects from estimated positions to further positions; and control the electrodes based on electrode potentials.

Abstract: Methods may comprise: identifying a fault indicator associated with a physical system; collecting first data related to a state of the physical system; applying a surrogate model to the first data to produce a plurality of potential fault modes; applying an optimization algorithm to the plurality of potential fault modes using a similarity metric to produce an input and a plurality of outputs, wherein each of the plurality of outputs corresponds to one of the plurality of potential fault modes, wherein the input provides differentiation between each of the plurality of outputs; applying the input to the physical system; collecting second data from physical system in response to applying the input; identifying a true mode of the physical system based on a comparison of the second data and the plurality of outputs; and diagnosing a fault of the physical system based on the true mode.

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: System and method that allow to jointly cause movement of multiple micro-and-nano-objects to desired positions are described. A high speed camera tracks the locations of the objects. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate positions of the objects; generate desired trajectories of the objects using an objective function; compare the desired chiplet positions with current positions and generate input signals to minimize the error between them; and map the control inputs to images that are projected on the array using a video project. The projected images activate or deactivate electrodes, as indicated by the control inputs.

Abstract: One embodiment can provide a method and system for estimating a remote quantity of interest (QoI). During operation, the system can receive, over a communication channel, a radio frequency (RF) signal carrying an estimate of the QoI measured by a sensor. The system can estimate probability distributions of a set of random channel parameters associated with the HF communication channel. The system can further reconstruct the estimate based on the probability distributions of the channel parameters and the received RF signal, determine a level of uncertainty associated with the reconstructed estimate, and combine reconstructed estimates from multiple sensors based on the determined level of uncertainty associated with each reconstructed estimate to output a combined estimate of the QoI.

Abstract: One embodiment provides a method for automated design of a physical system. The method can include obtaining design requirements associated with the physical system and iteratively performing, by a computer, a reinforcement learning (RL) process and a nonlinear optimization process to generate a design solution. The RL process can generate a topology represented as a model of the physical system using a modeling language. The generated topology can specify a number of components and connections among the components. The nonlinear optimization process can determine parameters of the components in the topology based on the model and a loss function. The method can further include outputting the design solution of the physical system based on the generated topology and the determined parameters of the components, thereby facilitating construction of the physical system.

Abstract: A method includes obtaining a binary code of a controller. The method also includes decompiling the binary code of the controller to generate a source code. The method further includes generating one or more abstract syntax trees based on the source code. The method further includes generating an interpretable model based on the one or more abstract syntax trees. The interpretable model is interpretable by subject matter experts.

Abstract: An analog circuit for solving optimization algorithms comprises three voltage controlled current sources and three capacitors, operatively coupled in parallel to the three voltage controlled current sources, respectively. The circuit further comprises a first inductor, operatively coupled in series between a first pair of the capacitors and the voltage controller current sources and a second pair of the capacitors and the voltage controller current sources. The circuit further comprises a second inductor, operatively coupled in series between the second pair of the capacitors and the voltage controller current sources and a third pair of the capacitors and the voltage controller current sources.

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 component library having a plurality of design components is received. Designs are predicted using the plurality of components using a machine learning model. The predicted designs comprise a subset of all possible designs using the plurality of components. A set of design criteria is received. At least one design solution is generated based on the set of design criteria and the predicted designs.

Abstract: System and method that allow to control density distributions of multiple particles (micro-or-nano-sized objects) to desired positions are described. A kernel density estimation (KDE) is used as a proxy for the initial particle density distribution and an optimal control problem is defined and solved using this approximation. A sequence of electrode electric potentials is computed so that the initial particle distribution is shaped into a target distribution after applying this sequence over time. The optimal control cost function is defined in terms of an L2 metric, with the L2 function that is used to compute the error between the particle density at the end of a time horizon and a target density. The KDE depends on the predicted trajectories of a set of particles, where the trajectory of a single particle is determined by a lumped, 2D, capacitive-based, nonlinear model describing the particle's motion.

Abstract: One embodiment provides a method and a system for diagnosing a digital circuit. During operation, the system can obtain a design of the digital circuit, generate a design of a diagnostic circuit by augmenting the design of the digital circuit based on a number of fault-emulating subcircuits, and convert the design of the diagnostic circuit to a design of a quantum oracle circuit. The system can further construct a quantum diagnostic circuit based on the design of the quantum oracle circuit and observe states of the quantum diagnostic circuit to determine probability distributions of one or more faults in the digital circuit.

Abstract: System and method that to shape micro-object density distribution (how densely the micro-objects are assembled in particular spatial regions) are provided. A high speed camera tracks existing object density distribution. An array of photo-transistor-controlled electrodes is used to generate a dynamic potential energy landscape for manipulating objects with both DEP and EP forces, and a video projector is used actuate the array. One or more computing devices are used to: process images captured by the camera to estimate existing density distribution of objects; receive a desired density distribution of micro-objects; define a model describing a variation of micro-object density over time due to capacitance-based interactions; generate a sequence of electrode potential that when generated would minimize error between the existing density distribution and a desired density distribution; and use the sequences of electrode potentials to actuate the electrodes.

Abstract: One embodiment provides a method and a system for diagnosing faults in a physical system. During operation, the system can create a fault-augmented model of the physical system by considering various potential faults, and it can generate a machine-learning model to predict an operation mode of the physical system using the outputs of the physical system. A respective operation mode corresponds to normal operation or a potential fault in the physical system. The system can generate a plurality of training samples based on the fault-augmented model, use the training samples to train the machine-learning model to learn a sequence of inputs and model parameters that minimizes an uncertainty of the predicted operation mode, and then apply the learned sequence of inputs and the trained machine-learning model on the physical system to determine the operation mode of the physical system.

Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.

Abstract: System and method that allow utilize machine learning algorithms to move a micro-object to a desired position are described. A sensor such as a high speed camera or capacitive sensing, tracks the locations of the objects. A dynamic potential energy landscape for manipulating objects is generated by controlling each of the electrodes in an array of electrodes. One or more computing devices are used to: estimate an initial position of a micro-object using the sensor; generate a continuous representation of a dynamic model for movement of the micro-object due to electrode potentials generated by at least some of the electrodes and use automatic differentiation and Gauss quadrature rules on the dynamic model to derive optimum potentials to be generated by the electrodes to move the micro-object to the desired position; and map the calculated optimized electrode potentials to the array to activate the electrodes.

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: The system and method described allow for real-time control over positioning of a micro-object. A movement of at least one micro-object suspended in a medium can be induced by a generation of one or more forces by electrodes proximate to the micro-object. Prior to inducing the movement, a simulation is used to develop a model describing a parameter of an interaction between each of the electrodes and the micro-object. A function describing the forces generated by an electrode and an extent of the movement induced due to the forces is generated using the model. The function is used to design closed loop policy control scheme for moving the micro-object towards a desired position. The position of the micro-object is tracked and taken into account when generating voltage patterns in the scheme.

Abstract: A system and method for performing hybrid reasoning to predict remaining useful life of a target system. During operation, the system measures, via a set of sensors associated with the target system, sensor signals before a prediction start time. The system updates, based on the measured sensor signals, a first set of parameters of a physics-based model associated with the target system. The system in response to determining that the target system current time is less than a prediction start time: apply a machine-learning model to estimate a second aspect of the health of the target system; and update a second set of parameters of the physics-based model. The system can perform a time simulation of the updated physics-based model to predict a wear/degradation pattern of the target system after the prediction start time; and determine, based on the predicted wear/degradation pattern, a remaining useful life of the target system.

Abstract: One embodiment provides a method and a system for automated design of a physical system. During operation, the design system obtains qualitative and quantitative design requirements associated with the physical system and inputs the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system. The topology specifies a number of components and connections among the components. The design system then determines parameters of the components based on the quantitative design requirements.