Hitachi, Ltd.

Sudhanshu Gaur

Joydeep Acharya

Long Gao

Daisuke Maeda

Yusuke Shomura

Yasutaka Serizawa

Takeshi Shibata

Qing Li

Clifford Tavares

Satoshi Katsunuma

Brian E Litzinger

John Simons

Karthik Channakeshava

Example implementations described herein involve systems and methods to select machine learning models that will be executed in a cellular Mobile Edge Computing for cellular enabled applications. In contrast to related art implementations, the example implementations described herein considers different data fidelities of received data due to the cellular wireless channel and also performs service resource allocation accordingly.

Optimizing machine learning as-a-service performance for cellular communication systems

Example implementations described herein involve systems and methods that can involve extracting features from each of a plurality of time-series sensor data, the plurality of time-series sensor data associated with execution of one or more operations; clustering the extracted features into a plurality of tasks that occur from execution of the one or more operations, each of the plurality of tasks associated with a clustering identifier (ID) from the clustering; and calculating a cycle time of the cycle based on the initiation and end of the cycle recognized by referencing a cycle pattern model, wherein the cycle pattern model comprises configuration information of a cycle including a set from a plurality of the clustering IDs.

Task and cycle time detection method and system

Example implementations involve a location system, which can involve associating each location of one or more unidentified targets detected from sensor data of the one or more sensors with identifiers corresponding to the transmitter of each of the one or more pairs of electronic devices, by calculating first distance relationships indicative of relationships of distances between the each location of the one or more unidentified targets detected from the sensor data of the one or more sensors and a reference point; calculating second distance relationships indicative of relationships of distances between the transmitter and the receiver of each of the one or more pairs of electronic devices; and associating the identifiers corresponding to the transmitter of the each of the one or more pairs of electronic devices with the each location of the one or more unidentified targets based on the first distance relationships and the second distance relationships.

Location system and method

A method for providing data to a client computing device from an edge computing device is discussed herein. The method may include performing a network proximity check regarding the client computing device associated with a request for data captured by the wideband sensor. The method may further include determining, based on at least one proximity metric associated with the client computing device, a route for data responsive to the request for data associated with the network proximity check, where the route is one of a route including the cloud storage or a route that does not include the cloud storage. The method may also include receiving the request for data captured by the wideband sensor associated with the network proximity check. The method may also include transmitting the data responsive to the request for data captured by the wideband sensor associated with the network proximity check to the client computing device through the determined route.

Video analysis and data delivery method

Example implementations described herein are directed to systems and methods for extracting signal in the presence of noise for industrial IoT systems. Through the example implementations described herein, the high-frequency band signal of the sensor output can be maintained despite data compression, while also retaining information regarding the condition of the machine. Example implementations can also generate compressed data pairs to synchronized downsampled envelopes and spectrum data.

Method for compressing wideband sensor signal

Systems and methods described herein are directed to minimizing the resource requirements for edge and network while keeping the accuracy of machine learning classifier by utilizing simulated test data. Once sufficient measured test data is collected by the server, the server instructs the edge computer to reduce the transmission of data received from the corresponding sensors.

System and signal processing method for failure mode identification

Example implementations described herein are directed to systems and methods for extracting signal in presence of strong noise for industrial Internet of Things (IoT) system especially for monitoring systems of consumable items such as lathe machines, coolers and so on. Example implementations can utilize a sawtooth mother Wavelet instead of usual wavelet analysis to cleanse the incoming sensor data, thereby allowing for the converting sensor data to feature values despite having heavy noise interference.

Method for extracting signal in presence of strong noise

Systems and methods described herein are directed to a specialized Internet of Things (IoT) device deploying both acoustic and radio wave signals. In example implementations described herein, camera data and acoustic sensor data is integrated to generate an acoustic sensor heatmap for the holistic sensing systems in an IoT area.

Holistic sensing method and system

Systems and methods involving integrating camera and acoustic sensor data, and automatically capturing the acoustic sensor heatmap for the holistic sensing systems in Internet of Things (IoT) systems. In particular, example implementations described herein capture the local sound noise environment or localized noise profiles (e.g., noise fingerprint) adaptively to the change of noise profiles and automatically apply captured noise profiles to the streaming noise reduction in signal processing for industrial IoT areas.

Adaptive acoustic sensing method and system

Example implementations described herein are directed to systems and methods for non-invasive data extraction from digital displays. In an example implementation, a method includes receiving one or more video frames from a video capture device capturing an external display, where the external display is independent the video capture device; determining one or more locations within the external display comprising time varying data of the external display; and for each identified location of the time varying data: determining a data type; applying one or more rules based on the data type; and determining an accuracy of the time varying data within the one or more frames based on the rules.

Non-invasive data extraction from digital displays

This invention aim to improves the flexibility of data flows management from sensor to cloud, datalake or other system, which can manage the overall data flows within the system and control them dynamically. As a result, it can reduce transmission cost and storage cost properly.

Method to manage data flow for edge-core distributed analytics systems

In some examples, a computing device may receive sensed data of a first sensor sent in a first transmission. The computing device may associate a first timestamp with the sensed data. Further, the computing device may receive, from other sensors, first signal strength information including first signal strength data and a first signal property related to the first transmission, and a second timestamp corresponding to detection of the first transmission. The computing device may receive, from other sensors, second signal strength information including second signal strength data and a second signal property related to a second transmission, and a third timestamp corresponding to detection of the second transmission. When the third timestamp is later than the first timestamp and the first signal property matches the second signal property, the computing device may indicate that a sensor that sent the second transmission is associated with an anomaly.

Wireless sensor network security

In some examples, a computing device may determine a prediction of a network outage of a network. The computing device may determine a priority of one or more data types expected to be received during the network outage. Further, the computing device may determine a latency category of the one or more data types expected to be received during the network outage. The computing device may store a data transmission rule for the one or more data types at least partially based on the priority and the latency category. The computing device may receive, from one or more data generators, during the network outage, data for transmission to the network. The computing device may transmit at least some of the received data to the network at least partially based on the data transmission rule.

Edge processing for data transmission

Example implementations described herein are directed to a system involving a cloud architecture and an edge architecture associated with one or more vehicles. The edge architecture can involve devices associated with the one or more vehicles and can conduct edge processing to determine, from Global Positioning Satellite (GPS) information, a proximity of the first apparatus to a first Geographic Information System (GIS) waypoint relative to a second GIS waypoint, generate index information representative of the proximity of the apparatus to the first GIS waypoint relative to the second GIS waypoint; and transmit the index information to the cloud architecture.

GIS based compression and reconstruction of GPS data for transmission from a vehicular edge platform to the cloud

A communications system includes a macro base station, a plurality of UEs (user equipment), a plurality of small cells, and a network through which the macro base station, the UEs, and the small cells communicate with each other, the small cells within a macro coverage area of the macro base station. The macro base station comprises a processor, a memory, and a small cell on/off module which is operable, for each small cell of the plurality of small cells, to: determine an interference metric for the small cell; if the determined interference metric meets a preset condition for the small cell, then determine a loss in signal strength to the UEs associated with the small cell caused by switching off the small cell; and judge whether to switch off the small cell based on at least one of the determined interference metric or the determined loss in signal strength.

Growing community of inventors

Santa Clara, CA, United States of America

Sudhanshu Gaur