Abstract: In example implementations described herein, the power of time series machine learning is used to extract the statistics of Programmable Logic Controller (PLC) data and external sensor data. The accuracy of time series machine learning is improved by manufacturing context-dependent segmentation of the time series into states which is factory may be in. The invention can capture subtle trends in these time series data and be able to classify them into several outcomes from ICS security attacks to normal anomalies and machine/sensor failures.

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: 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: 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 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 reducing the MME signaling overhead associated with paging and tracking area update procedures and also conserves UE battery life. This can be realized by using non-network data and applying predictive analytics algorithms to track the most probable locations of an IDLE UE, which may increase the overall efficiency of the network. In example implementations, MME signaling overhead associated with paging and TA update procedures may also be reduced, which can also conserve UE battery life.

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: 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.

Abstract: Example implementations described herein are directed to reducing the MME signaling overhead associated with paging and tracking area update procedures and also conserves UE battery life. This can be realized by using non-network data and applying predictive analytics algorithms to track the most probable locations of an IDLE UE, which may increase the overall efficiency of the network. In example implementations, MME signaling overhead associated with paging and TA update procedures may also be reduced, which can also conserve UE battery life.

Abstract: The present invention provides a method for use in a wireless communication system that supports orthogonal frequency division multiplexing (OFDM) of transmissions over a plurality of subcarriers. One embodiment of the method includes a network entity that allocates one or more of a plurality of sub-bands for communication with an access terminal. Each of the sub-bands includes one or more of the subcarriers and is associated with a corresponding a pseudo-random sequences. The network entity can also transmit one or more of the plurality of pseudo-random sequences over an air interface. The transmitted pseudo-random sequence indicate the sub-bands that are allocated for communication with the access terminal.

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.

Abstract: Example implementations are directed to systems and methods based on physical broadcast channel (PBCH) muting are utilized to avoid frequent cell selection/reselection and handover in a LTE-advanced heterogeneous network. In the example implementations, a pico eNB that is fully covered by a macro eNB or other pico eNBs transmits blank PBCH such that it is inaccessible to the UEs who perform cell selection/reselection. Furthermore, a macro eNB may handover a UE to the inaccessible pico eNB by signalling the necessary information to the UE to detect the inaccessible pico eNB. Frequent cell selection/reselection and handover may therefore be avoided in a dense deployment situation.

Abstract: Embodiments described herein are directed to a power control scheme for Long Term Evolution Advanced (LTE-A) heterogeneous networks to reduce the interference from macro base stations (BS) to pico user equipment (UE). The embodiments described herein may be used to develop LTE-A heterogeneous networks to balance the achievable throughput between macro and pico UEs and may thereby improve the overall system performance.

Abstract: The Long Term Evolution Advanced (LTE-A) network, is a heterogeneous network, where macro and pico base stations (BSs) coexist to improve spectral efficiency per unit area. Systems and methods described herein attempt to provide a solution to the interference coordination problem between macro BSs and pico user equipments (UEs). Specifically, the systems and methods conduct interference coordination based on the concept of almost blank subframe (ABS), which is supported by the LTE-A standard. The macro BSs choose their ABS configurations in a cooperative way such that the overall system throughput is optimized.

Abstract: Example embodiments described herein are directed to systems and methods by which a group of base stations (BS) can configure pilot signals in time and time-frequency, using interference management resources (IMR) and/or channel state information reference signal (CSI-RS) resources, so that the user equipment (UE) such as mobiles and laptops can measure certain possible channel quality indicators (CQI) that correspond to specific channel and interference conditions that can arise during actual data submission. Using these values, example embodiments utilize an interpolation algorithm by which the group of base stations can estimate other possible CQI corresponding to a different set of channel and interference conditions.

Abstract: A base station (BS) comprises: an interface to receive radio resource management (RRM) measurement from user equipment on received reference signal (RS) strength for each BS of one or more other BSs in a surrounding area of the user equipment; an X2 interface to receive transmission information of almost blank subframes and corresponding cell-specific reference signal (CRS) resource element (RE) locations from the one or more other BSs; and a controller to manage bit-level PDSCH (Physical Downlink Shared Channel) muting information, which includes identifying PDSCH resource elements (REs) that suffer the most from CRS interference arising from other BSs and that are to be subjected to bit-level muting, and allocating to the identified REs a number of bits less than the number of bits allocated to normal REs, and to transfer data with the two levels of bit allocations to various PDSCH REs to the user equipment.

Abstract: Example embodiments described herein are directed to systems and methods by which a group of base stations (BS) can configure pilot signals in time and time-frequency, called channel state information reference signal (CSI-RS) resources, so that the user equipment (UE) such as mobiles and laptops can measure certain possible channel quality indicators (CQI) that correspond to specific channel and interference conditions that can arise during actual data submission. Using these values, example embodiments utilize an interpolation algorithm by which the group of base stations can estimate other possible CQI corresponding to a different set of channel and interference conditions.