Abstract: 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.

Abstract: The invention relates to a system and method for automated software testing based on ML. The system comprises a software design module 101 which is configured to provide at least one of business requirement, flow document etc. The requirement parser 102 extracts the actionable items from output of the software design module 101. A ML engine 103 uses supervised ML algorithm to map actionable items with the historic test suites. The test suites and test cases are stored in a NoSQL database. Further, a test design module 104 is configured to create automatic test case design based on ML and assign priorities to the test cases using the parser. A human feedback 105 to the system helps to make the system learns or adjusts the decision making to be more precise.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The invention provides a system and method for automated software testing based on Machine Learning (ML). The system automatically picks up results of the software test automation reports from software test automation framework. The report parser parses the failures from the report. A ML engine compares them with the failures that are known or present in the NoSQL database. After the creation of bug ticket in the defect-tracking tool, an automated notification system notifies the stakeholders via email or instant messaging about the status of the respective ticket. A feedback to the system by software test engineer helps to make the system learn or adjust the decision making to be more precise.

Abstract: 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.

Abstract: 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.

Abstract: The invention relates to a system and method for automated software testing based on ML. The system comprises a software design module 101 which is configured to provide at least one of business requirement, flow document etc. The requirement parser 102 extracts the actionable items from output of the software design module 101. A ML engine 103 uses supervised ML algorithm to map actionable items with the historic test suites. The test suites and test cases are stored in a NoSQL database. Further, a test design module 104 is configured to create automatic test case design based on ML and assign priorities to the test cases using the parser. A human feedback 105 to the system helps to make the system learns or adjusts the decision making to be more precise.

Abstract: 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.

Abstract: The invention provides a system and method for automated software testing based on Machine Learning (ML). The system automatically picks up results of the software test automation reports from software test automation framework. The report parser parses the failures from the report. A ML engine compares them with the failures that are known or present in the NoSQL database. After the creation of bug ticket in the defect-tracking tool, an automated notification system notifies the stakeholders via email or instant messaging about the status of the respective ticket. A feedback to the system by software test engineer helps to make the system learn or adjust the decision making to be more precise.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In some examples, a computing device may receive sensed data information sent in a transmission by a first sensor, the sensed data information including a sensor identifier and sensed data of a first sensor. The computing device may associate a first timestamp with the sensed data information. Further, the computing device may receive, from other sensors, radio signal strength information including signal strength data related to the transmission and a second timestamp corresponding to detection of the transmission. The computing device may determine a location of the first sensor based on the signal strength data received from the other sensors. In addition, the computing device may associate the location with the sensor identifier of the first sensor based on comparing the first timestamp with the second timestamp. In some cases, one or more second sensors may forward the transmission from the first sensor to the computing device.

Abstract: 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.

Abstract: 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.

Abstract: Example implementations involve a quality analysis and optimization module to monitor the health of the wireless channels in WLAN networks. Example implementations involve a framework for deriving a model of wireless link quality metrics as a function of higher layer transport protocols metrics. Example implementations then utilize the model to analyze and perform root cause analysis and optimization of WLAN networks to improve the quality of experience of wireless users.

Abstract: 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.