Mitsubishi Electric Research Laboratories, Inc.

Kyeong-Jin Kim

Philip Orlik

Kieran Parsons

Karl Berntorp

Hongbo Sun

Stefano Di Cairano

Chunjie Duan

Jianlin Guo

Ghulam Bhatti

Marcel Menner

Marcus Greiff

Atulya Yellepeddi

Stefano Di Calrano

Tong Huang

Sriramya Bhamidipati

Ferdous Pervej

Remy Zawislak

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). It involves selecting agents, associating them with RSUs based on proximity, and transmitting training data and deadlines. Agents train models locally, which are then aggregated through RSUs to refine the global model to desired precision. 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.

Communication and computation aware distributed machine learning for vehicular networks

Embodiments of the present disclosure disclose a method and a system for tracking the position of the one or more moving objects. The method includes collecting GNSS measurement data of satellite signals transmitted from multiple satellites. The method further includes extracting values of a plurality of features from the GNSS measurement data. The method includes mapping the extracted values of the plurality of features to a source domain. The method includes classifying the mapped transformed plurality of features using a neural network. The neural network is trained over simulated data sampled from a source domain. The method includes identifying multipath measurements of the GNSS measurement data based on classification of the corresponding mapped transformed plurality of features. The method includes tracking the position of the one or more moving objects by processing identification of GNSS measurement data affected by multipath.

Method and system for position estimation using domain adaptation

A time synchronization system an input interface configured to receive pseudorange measurements from a set of antennas of at least one Global Navigation Satellite System (GNSS) receiving system that operates the antennas at known positions according to a clock with an unknown time bias to receive signals from satellites in sight of the antennas and an output interface configured to output the determined time bias. The time synchronization system also includes a processor configured to compare the pseudorange measurements of different antennas to produce an antenna-specific clock offset for each of the antennas, correct the pseudorange measurements of the antennas according to the antenna-specific clock offsets of corresponding antennas to produce corrected pseudorange measurements, and determine the time bias of the clock using the corrected pseudorange measurements.

GNSS-based timing synchronization

Systems and methods for coordinating network and control parameters of a power distribution system (PDS) with interconnected microgrids in response to a subset of interconnected microgrids entering island-mode due to a predicted future disaster, generating samples of network and control parameter combinations, determining optimal adjustments of network and control parameters with respect to the disaster condition, determining optimal set of network and control parameters to be reinforced or adjusted, activating the parameter adjustments and reinforcements on the determined tie lines and PCCs of the microgrids.

Methods and systems for a holistic framework for parameter coordination of interconnected microgrid systems against disasters

A signal received via a fading channel is decoded in radio frequency receiver by first demodulating the signal to a baseband signal, and serial to parallel converting the baseband signal to a precoded signal. Then, a channel matrix is estimated from the precoded signal, and symbols are detected in the precoded signal using the channel matrix. The decoding uses probabilistic data association. The detecting initializes a probability distribution for each symbol, and selects an ordering in which to update the probability distributions. The probability distribution for each symbol are updated according to the ordering, until a termination condition is reached, and then demapped and parallel to serial converted to estimate a sequence of bits used to generate the signal.

Method and system for communicating data wirelessly using probabilistic data association

Interference in a wireless network is detected by first applying during a detection stage a two-sample Kolmogorov-Smirnov (KS) test to samples obtained from a channel in the wireless network to obtain a KS statistic. When the KS statistic is larger than a predetermine threshold, it is determined that the samples include narrowband interference, and wideband otherwise.

Method for detecting, classifying, and reducing interference in wireless networks

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Lexington, MA, United States of America

Kyeong-Jin Kim