Abstract: A photo-detecting device includes a first semiconductor layer with a first dopant, a light-absorbing layer, a second semiconductor layer, and a semiconductor contact layer. The second semiconductor layer is located on the first semiconductor layer and has a first region and a second region, the light absorbing layer is located between the first semiconductor layer and the second semiconductor layer and has a third region and a fourth region, the semiconductor contact layer contacts the first region. The first region includes a second dopant and a third dopant, the second region includes second dopant, and the third region includes third dopant. The semiconductor contact layer has a first thickness greater than 50 ? and smaller than 1000 ?.

Abstract: A semiconductor device comprises a first semiconductor structure, a second semiconductor structure located on the first semiconductor structure, and an active layer located between the first semiconductor structure and the second semiconductor structure. The first semiconductor structure has a first conductivity type, and includes a plurality of first layers and a plurality of second layers alternately stacked. The second semiconductor structure has a second conductivity type opposite to the first conductivity type. The plurality of first layers and the plurality of second layers include indium and phosphorus, and the plurality of first layers and the plurality of second layers respectively have a first indium atomic percentage and a second indium atomic percentage. The second indium atomic percentage is different from the first indium atomic percentage.

Abstract: A multi-piece pre-assembled raft foundation is provided and includes pre-assemble bottom layer rebars of foundation slab, foundation steel columns, upper layer rebars of the foundation slab, and foundation rebars. The pre-assembled raft foundation is transported to a construction site for final assembly. A construction method of the multi-piece pre-assembled raft foundation is also provided.

Abstract: A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Abstract: A traceback solution is provided. For a network of autonomous systems, the traceback solution traces the autonomous system path taken by traffic flows. Every link in the traceback path is created, verified, and audited by autonomous systems. Multiple autonomous systems may take part in the process, making the system robust against fake information. The database used to store the validated traceback paths is a decentralized and distributed storage. Multiple copies of the database may be maintained by the network of autonomous systems. The database may be accessible by any participating autonomous system; and is not accessible from outside the network of autonomous systems. The traceback solution achieves both validation and non-repudiation property among the ASes. The traceback solution mitigates some important attack scenarios that might be targeted specifically at the traceback solution.

Abstract: A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Abstract: A method for processing data, task processing system and electronic equipment are disclosed.

Abstract: A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A traceback solution is provided. For a network of autonomous systems, the traceback solution traces the autonomous system path taken by traffic flows. Every link in the traceback path is created, verified, and audited by autonomous systems. Multiple autonomous systems may take part in the process, making the system robust against fake information. The database used to store the validated traceback paths is a decentralized and distributed storage. Multiple copies of the database may be maintained by the network of autonomous systems. The database may be accessible by any participating autonomous system; and is not accessible from outside the network of autonomous systems. The traceback solution achieves both validation and non-repudiation property among the ASes. The traceback solution mitigates some important attack scenarios that might be targeted specifically at the traceback solution.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A method, a computer-readable medium, and an apparatus for IP traceback are provided. The apparatus may generate a group public key shared by a plurality of routers controlled by a service provider. The apparatus may generate a unique private signing key for a router of the plurality of routers. The private signing key may be used to generate a group signature for a session of network traffic. The group public key may be applied to the group signature to identify the service provider. The apparatus may identify the router by applying the group public key and a master secret key to the group signature. The apparatus may deploy preventive or mitigate action on the identified router.

Abstract: A Self-Organizing Network (SON) collects data pertaining to a first number of cells of a wireless network. The SON splits the collected data into a second number of groups, and, for each of the second number of groups, repeatedly set a third number of clusters to a different number between a low limit and a high limit. The SON, for each of the settings, clusters the cells into the third number of clusters and trains a deep neural network to perform a regression analysis on the third number of clusters. For each of the second number of groups, the SON also determines an optimum number of clusters based on the regression analyses, re-clusters the cells into the optimum number of clusters; and tunes engineering parameters based on the re-clustering to optimize performance of the wireless network and quality of experience pertaining to the wireless network.

Abstract: A method, a device, and a non-transitory storage medium for estimating voice call quality include performing automatic speech recognition, for each of a plurality of voice calls, to generate recognized text for both an originating device acoustic signal and a receiving device acoustic signal. The recognized text for both the originating device acoustic signal and the receiving device acoustic signal are compared to the reference text to identified recognition errors and a voice call quality score for each of the originating device acoustic signal and the receiving device acoustic signal are determined. A correlation between the network conditions and the voice call quality scores is then determined.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A method, a device, and a non-transitory storage medium for estimating voice call quality include performing automatic speech recognition, for each of a plurality of voice calls, to generate recognized text for both an originating device acoustic signal and a receiving device acoustic signal. The recognized text for both the originating device acoustic signal and the receiving device acoustic signal are compared to the reference text to identified recognition errors and a voice call quality score for each of the originating device acoustic signal and the receiving device acoustic signal are determined. A correlation between the network conditions and the voice call quality scores is then determined.

Abstract: A Self-Organizing Network (SON) collects data pertaining to a first number of cells of a wireless network. The SON splits the collected data into a second number of groups, and, for each of the second number of groups, repeatedly set a third number of clusters to a different number between a low limit and a high limit. The SON, for each of the settings, clusters the cells into the third number of clusters and trains a deep neural network to perform a regression analysis on the third number of clusters. For each of the second number of groups, the SON also determines an optimum number of clusters based on the regression analyses, re-clusters the cells into the optimum number of clusters; and tunes engineering parameters based on the re-clustering to optimize performance of the wireless network and quality of experience pertaining to the wireless network.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.