Abstract: A robotic system for manipulating an object with a robotic manipulator is provided. The robotic system is configured to collect a digital representation of a task for manipulating the object; solve a robust control problem to optimize a sequence of control forces to be applied by the robotic manipulator to change a state of the object, where an evolution of the state of the object is governed by a stochastic complementarity system modeling the task with a predefined probability. The robust control problem optimizes a cost function to generate the sequence of control forces performing the task subject to joint chance constraints including a first chance constraint on the state of the object being manipulated and a second chance constraint on stochastic complementarily constraints modeling manipulation of the object. The robotic system is further configured to control the manipulation of the object based on the sequence of control forces.

Abstract: Embodiments of the present disclosure disclose a system and method for localization of a target sound event. The system collects a first digital representation of an acoustic mixture of sounds of a plurality of sound events, by using an acoustic sensor. The system receives a second digital representation of a sound corresponding to the target sound event. Further, the first digital representation and the second digital representation are processed by a neural network to produce a localization information indicative of a location of an origin of the target sound event with respect to a location of the acoustic sensor.

Abstract: Embodiments of the present invention disclose a method and system for performing video anomaly detection by training neural networks. The method includes collecting a video of one or more digital images from a source domain. The method includes obtaining a set of images of foreground objects present in the video. In addition, the method includes training of a first neural network to predict frames for the one or more digital images in the video. The first neural network is trained using a future frame prediction module that predicts frames for the one or more digital image. The method includes training of a second neural network to classify the predicted frame as normal and to classify the synthesized pseudo anomaly frame as abnormal. The method includes performing video anomaly detection based on training of the first neural network and the second neural network.

Abstract: A Bluetooth-enabled device is provided, the Bluetooth-enabled device being configured to control a radio frequency (RF) chain during a first period to receive at a single antenna selected from a plurality of antennas, a constant tone extension (CTE) signal of multiple frames transmitted by the Bluetooth-enabled transmitter over multiple frequencies. Further the RF chain is controlled during a second period to switch among the plurality of antennas to receive the CTE signal at each of the plurality of antennas. An initial time-of-flight (ToF) data of the CTE signal is determined from first samples of the CTE signal received during the first period. Further, the Bluetooth-enabled transmitter is localized with respect to a location of the Bluetooth-enabled device using a signal model connecting samples of the CTE signal with an unknown angle-of-arrival of the CTE signal received at specific times, an unknown ToF conditioned on the initial ToF data.

Abstract: A system for controlling or optimizing the performance of a vapor compression system by modifying the actuator commands via an output interface, that realizes thermofluid property functions and their derivatives as spline functions which are represented in a coordinate system that is aligned with a fluid saturation curve. The system includes an interface configured to receive measurement data from sensors, a memory configured to store thermofluid property data and computer-executable programs including a B-spline method, and a processor for performing the computer-implemented method. The processor is configured to take as input two thermofluid property variables, and compute a coordinate transformation in which one axis of the coordinates is aligned with the liquid and vapor saturation curves. In the saturation-curve aligned coordinates, a spline function represents the thermofluid property function, with coefficients and knots stored in memory.

Abstract: A computer-implemented method is provided for generating device parameters of circuits using a pretrained reinforcement learning (RL) agent composed of a graph neural network (GNN) and a fully connected neural network (FCNN). The method is performed by steps including acquiring inputs with respect to a set of desired specifications or one desired specification of a circuit, device parameters, a fixed topology of the circuit and providing the inputs to the RL agent. The desired circuit description includes a graph modeling the topology of the circuit and device parameters of the circuit, and the desired specifications include gain, bandwidth, phase margin, power consumption, output power and power efficiency.

Abstract: A vehicle includes an advanced driver-assistance system (ADAS) configured to intervene in an operation of the control system by complementing or overriding the driving input in response to detecting a driving condition dependent on a calibration parameter indicative of a preference of execution of the driving maneuver. The ADAS is calibrated based on a crowd-local distribution function of the calibration parameter indicative of a distribution of the preference of execution of the driving maneuver by other drivers of other vehicles at a specific location or a specific environment in response to detecting that the vehicle approaches the specific location or the specific environment.

Abstract: An imaging controller is provided for segmenting instances from depth images including objects to be manipulated by a robot. The imaging controller includes an input interface configured to receive a depth image that includes objects, a memory configured to store instructions and a neural network trained to segment instances from the objects in the depth image, and a processor, coupled with the memory, configured to perform the instructions to segment a pickable instance using the trained neural network.

Abstract: A computer-implemented method is provided for training a global machine learning model using a learning server and a set of vehicle agents connected to roadside units (RSUs). The method includes steps of selecting vehicle agents from a pool of the vehicle agents connected to the RSUs, associating the selected vehicle agents and the RSUs respectively based on distances from the selected vehicle agents to the RSUs configured to provide measurements of the distances to the learning server, and transmitting a global model, a selected agent set and deadline thresholds in each global training round to the RSUs configured to transmit the global model and training deadlines to the selected vehicle agents. The associated RSUs compute the training deadlines of the corresponding selected vehicle agents and the selected vehicle agents locally train the global model independently using the local datasets collected by the on-board sensors of the selected vehicle agents to generate locally trained models.

Abstract: A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.

Abstract: A magnetic particle imaging system includes a field free region generator and an excited magnetic field generator. The field free region generator generates a field free line with a direction of linear extension of a field free region as a direction of extension. The excited magnetic field generator generates an excited magnetic field in the field free line generated by the field free region generator. The excited magnetic field generator includes a first excited magnetic field generation unit and a second excited magnetic field generation unit. The first excited magnetic field generation unit and the second excited magnetic field generation unit are spaced from each other in the direction of extension of the field free line.

Abstract: Embodiment of the present disclosure disclose a tomographic imaging system for reconstructing an image of an internal structure of an object. An incident wavefield is transmitted into the object occupying a background domain embedding the object. The incident wavefield is scattered into multiple scattered wavefield by the object. The incident and scattered wavefields are measured as a total wavefield. The total wavefield propagates through a computational domain and a residual domain in the background domain that are defined by cross-domain and residual measurement operators. The total wavefield is used for the image reconstruction. The image is reconstructed by solving an optimization problem corresponding to the computational domain. The optimization problem is solved iteratively to minimize a difference between the total wavefield and a wavefield synthesized using a measurement operator and a Green's function operator from the reconstructed image.

Abstract: A method for communication between a communication device and a base station in a private 5G network where the communication device and the base station have a base period of transmission defined there between as a minimal scheduling period, comprises transmitting location data of the communication device in a last subframe of a current communication period of the communication device. The method further comprises receiving resource allocation data including a transmission period widening factor for one or more subsequent communication periods of the communication device and computing a semi-persistent scheduling (SPS) period as a subsequent communication period of the one or more subsequent communication periods, based on the transmission period widening factor and the base period of transmission. Uplink communication is performed in the one or more subsequent periods, based on the resource allocation data and the SPS period.

Abstract: A method for semi-persistent scheduling (SPS) based resource allocation in a private 5G network comprises receiving location data of a plurality of user equipment (UEs) in the private 5G network in a last subframe of respective current communication periods of the UEs and determining distance distribution of the UEs in a subsequent communication period of a candidate UE of the plurality of UEs, based on the respective location data of the UEs. The method further comprises determining signal to noise ratio expectation of the UEs for the subsequent communication period, solving an optimization problem optimizing an expectation of data to be carried by each of the one or more SPS channels in the subsequent communication period, to obtain scheduling result for each of the one or more SPS channels and generating resource allocation data for the candidate UE based on the scheduling result.

Abstract: A system for designing a circuitry configuration of heat-exchanger units includes an interface to acquire design parameters the heat-exchanger units, a memory to store computer-executable programs including a relaxed decision diagram formation module, and a processor, in connection with the memory, configured to perform the computer-executable programs. The computer-executable programs include steps of providing a configuration of the heat-exchanger units, providing the design parameters of the heat-exchanger units acquired via the interface, generating a relaxed decision diagram based on the design parameters, creating constraints with respect to connections of the heat-exchanger units according to the relaxed decision diagram, and generating feasible configurations of the heat-exchanger units by a mixed-integer-programing method using the constraints.

Abstract: A computer-implemented method of training an autoencoder to recover missing data is provided. The autoencoder includes an encoder for encoding its inputs into a latent space and a decoder for decoding the encodings from the latent space. The method comprises creating a first training set including a valid data set of multiple dimensions, and training the encoder and the decoder in a first training stage using the first training set to reduce a difference between the valid data set provided to the encoder and a data set decoded by the decoder. The method further comprises creating a second training set comprising an invalid data set, and training the encoder in a second training stage using the second training set to reduce a difference between encodings of valid data instances and encodings of their corresponding invalid data instances.

Abstract: A stochastic model predictive controller (SMPC) estimates a current state of the system and a probability distribution of uncertainty of a parameter of dynamics of the system based on measurements of outputs of the system, and updates a control model of the system including a function of dynamics of the system modeling the uncertainty of the parameter with first and second order moments of the estimated probability distribution of uncertainty of the parameter. The SMPC determines a control input to control the system by optimizing the updated control model of the system at the current state over a prediction horizon and controls the system based on the control input to change the state of the system.

Abstract: A method and system for activating thrusters of a vehicle for trajectory-tracking control of the vehicle. A transfer orbit generator to generate a transfer orbit for the vehicle from an initial orbit to a target orbit, and a feedback stabilization controller. Compute the target orbit for the vehicle about the celestial body. Compute a free trajectory with patch points along the free trajectory using a free trajectory module, each patch point includes a position and a velocity. Determine a feedback gain at each patch point using a feedback gain module, wherein a state penalty function at each patch point is set to match a state uncertainty function at the same patch point. Apply the feedback gain at each patch point to map the position and the velocity at each patch point to delta v commands, to maintain the target orbit using a feedback stabilization controller.

Abstract: A soft gripper including tentacles, each tentacle includes lower and upper members connected by a connector. Each member includes guide discs, and each guide disc includes a ring with passthrough holes, and a spacer located in a donut hole of the ring with passthrough holes, the passthrough holes collectively define cable pathways. The connector includes a center thru-hole and transfer channels. Cables have proximal ends attached to actuators and extend through apertures of a baseplate located at a proximal end of the lower member. A set of lower cables extend through the lower ring passthrough holes to couple to a distal lower guide disc. A set of upper cables extend through the lower spacer passthrough holes, through the transfer channels to the upper ring passthrough holes to couple to a distal upper guide ring, and an end cap is attached to the distal end of the upper member.

Abstract: A controller and a method for optimizing a controlled operation of a system performing a task is provided. The method for optimizing the controlled operation of the system comprises accessing a probabilistic distribution of a performance function trained to provide a relationship between different combinations of control parameters for controlling the system and their corresponding costs of operation, selecting a combination of control parameters from the different combinations of control parameters, such that the selected combination of control parameters is having the largest likelihood of being optimal at the probabilistic distribution of the performance function. The method further comprises controlling the system using the selected combination of the control parameters and modifying the probabilistic distribution of the performance function conditioned on the selected combination of the control parameters and the corresponding cost of operation.