Abstract: The present disclosure provides a zoom device and a lighting device. The zoom device includes a first lens assembly, a connecting frame, a first gear assembly, a second gear assembly and a third gear assembly. The second gear assembly and the third gear assembly mesh with the first gear assembly and connect with the first lens assembly and the connecting frame respectively, the first lens assembly is disposed between the first lens assembly and the connecting frame, and is configured to drive the second gear assembly and the third gear assembly to adjust a distance between the first lens assembly and the connecting frame. When focusing, mechanisms located on two sides of the first gear extend and contract synchronously, a focusing distance is increased, a problem of focus lags is alleviated, fluency and efficiency is improved, and the zoom device is suitable for focusing processing of large-size lighting devices.

Abstract: A method for knowledge representation and deduction of service logic includes: generating, based on a knowledge representation model, a semantic graph corresponding to conceptual-layer service logic, where the semantic graph includes one or more types of nodes and edges for connecting the one or more types of nodes, and the nodes include at least a node of a variable type; generating, based on the semantic graph and a physical table to which a service object is mapped, an instance graph, where the instance graph includes the nodes and edges in the semantic graph; generating executable code based on a service logic relationship between the nodes in the instance graph; and determining, based on the executable code and a data instance corresponding to a node whose in-degree is 0 in the instance graph, a data instance corresponding to each node in the instance graph.

Abstract: A method of engineering forecast and analysis is provided. The method includes: generating a forecasting model based on financial or business information obtained through a user interface, where the forecasting model expresses a logical determining and/or calculation rule based on a structure of a high-order directed graph and by using a plurality of nodes, attributes of the plurality of nodes, and a relationship between every two of the plurality of nodes, and the forecasting model expresses a forecasting path through a directed edge between the nodes; and configuring a common attribute of the forecasting model, an attribute of the forecasting path, and a personalized attribute of the node based on instantiation configuration information.

Abstract: A variable gain amplifier and an electronic apparatus. The variable gain amplifier includes a first transconductance stage circuit and a second transconductance stage circuit, where the first transconductance stage circuit includes a first amplifying circuit and a second amplifying circuit, the second transconductance stage circuit includes a third amplifying circuit and a fourth amplifying circuit, the first amplifying circuit and the fourth amplifying circuit form a differential input pair, and the second amplifying circuit and the third amplifying circuit form a differential input pair, and where each amplifying circuit of the first amplifying circuit, the second amplifying circuit, the third amplifying circuit, and the fourth amplifying circuit includes a plurality of parallel transistors, and bias control of the plurality of transistors is independent of each other.

Abstract: This application discloses a variable capacitor, a reflection-type phase shifter, and a semiconductor device, which relate to the technical field of electronics, so as to resolve the problem that a capacitance value of a variable capacitor is sensitive to changes in PVT. The variable capacitor includes: a first comb structure and a first set of fingers, where the first comb structure includes a plurality of comb teeth, the first set of fingers includes at least one finger, and the finger in the first set of fingers is disposed between at least two comb teeth of the first comb structure, without electrical contact; a second comb structure and a second set of fingers, where the second comb structure includes a plurality of comb teeth.

Abstract: Example temperature compensation circuits and a phased array apparatus are described. One example temperature compensation circuit is applied to a signal processing path. The example temperature compensation circuit includes a temperature detection circuit, a temperature conversion circuit, and a passive variable attenuator. The passive variable attenuator is configured to be connected in series in the signal processing path. The temperature detection circuit is configured to generate a temperature signal and a reference signal, and output the temperature signal and the reference signal to the temperature conversion circuit. The temperature signal monotonically changes with a temperature of the signal processing path. The temperature conversion circuit is configured to generate a control signal based on the temperature signal and the reference signal.

Abstract: A variable gain amplifier and an electronic apparatus. The variable gain amplifier includes a first transconductance stage circuit and a second transconductance stage circuit, where the first transconductance stage circuit includes a first amplifying circuit and a second amplifying circuit, the second transconductance stage circuit includes a third amplifying circuit and a fourth amplifying circuit, the first amplifying circuit and the fourth amplifying circuit form a differential input pair, and the second amplifying circuit and the third amplifying circuit form a differential input pair, and where each amplifying circuit of the first amplifying circuit, the second amplifying circuit, the third amplifying circuit, and the fourth amplifying circuit includes a plurality of parallel transistors, and bias control of the plurality of transistors is independent of each other.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: A plurality of access points located within a building structure may provide various location-based services, such as navigation, to mobile devices within the building structure. To more accurately determine the location of the plurality of access points, a floorplan of the interior of the building structure may be obtained. A plurality of images of the interior of the building structure is then obtained. At least one of the images includes an image of at least one of the plurality of access points. A plurality of reference points may also be located within the building structure and obtained. Based on the plurality of images, reference points, and the floorplan, a composite image of the interior of the structure is generated. Based on the composite image, the location of each of the plurality of access points is determined.

Abstract: Techniques for generating a prediction for a location of a mobile device includes a network controller obtaining signal strength information and angle-of-arrival information for mobile devices from a plurality of access points disposed at a location that includes two or more zones. The network controller extracts features from the obtained information. The features indicate whether the mobile devices are more likely in a first zone than in one or more of second zones of the two or more zones. The network controller generates a software model for classifying a mobile device into one of the two or more zones by applying a computer learning process to the extracted features. The network controller uses the software model to generate a prediction indicating that a first mobile device is in one of the two or more zones, and performs an action at the location based on the prediction.

Abstract: A plurality of access points located within a building structure may provide various location-based services, such as navigation, to mobile devices within the building structure. To more accurately determine the location of the plurality of access points, a floorplan of the interior of the building structure may be obtained. A plurality of images of the interior of the building structure is then obtained. At least one of the images includes an image of at least one of the plurality of access points. A plurality of reference points may also be located within the building structure and obtained. Based on the plurality of images, reference points, and the floorplan, a composite image of the interior of the structure is generated. Based on the composite image, the location of each of the plurality of access points is determined.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: In one embodiment, a device determines that location accuracy performance of an indoor positioning system deployment is below a predefined threshold. The device obtains characteristic data for the indoor positioning system deployment. The device identifies, by using the characteristic data as input to a machine learning model, one or more contributing factors from the characteristic data for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold. The device initiates a remediation action based on the identified one or more contributing factors for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold.

Abstract: In one embodiment, a device determines that location accuracy performance of an indoor positioning system deployment is below a predefined threshold. The device obtains characteristic data for the indoor positioning system deployment. The device identifies, by using the characteristic data as input to a machine learning model, one or more contributing factors from the characteristic data for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold. The device initiates a remediation action based on the identified one or more contributing factors for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold.

Abstract: It may be determined that each of a first plurality of devices was stationary at a location based on received first location information. Next, a first estimated ground truth may be determined for each of the first plurality of devices at the location based on the first location information corresponding to each of the respective first plurality of devices at the location. A first plurality of location estimation errors may then be determined for each of the first plurality of devices at the location respectively based on the first location information corresponding to each of the respective first plurality of devices at the location and the first estimated ground truth for each of the respective first plurality of devices at the location. Next, each of the determined first plurality of location estimation errors for each of the first plurality of devices at the location may be aggregated to provide a first location accuracy measurement for the location.

Abstract: Determining a location of a user device comprises a wireless computing system supported by an access point. The wireless computing system receives a signal from the user device. The system estimates a location of the user device based on RSSI and calculates a boundary around the estimated location. The wireless computing system selects a plurality of sections inside of the boundary and performs a coarse calculation of a location of the user device based on an angle of arrival of the received signal. The system determines sections of the plurality of sections that have results from the coarse calculation that are more likely to be a location of the user device. The system performs a fine calculation of the location based on the angle of arrival of the received signal within each of the sections. The system identifies a particular section as the location of the user device.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: Determining a location of a user device comprises a wireless system supported by wireless access points receiving signals from a user device. The wireless system estimates a location of the user device based on a coarse calculation based on an angle of arrival of the received signal and determines if the user device is in an area under an access point. The wireless system identifies received signal strength indicator values for the user device and uses the values and the calculated location of the user device to improve calibration of a received signal strength location model. If the system determines that the user is not in the area under the access point, the wireless system combines the coarse calculation location, a received signal strength determined location, and a previous received signal strength determined location, and estimates a location based on the combined calculations.