Abstract: A current transmission assembly and a current transmission system are provided. The current transmission system includes the current transmission assembly and a circuit board structure. The current transmission assembly includes a pluggable component, at least one conductor component, and at least one electrically connecting component. The pluggable component includes a housing, two sets of electrically conductive arms, and two connecting members. Each of the two sets of electrically conductive arms is disposed inside the housing. Each of the conductor components includes an electrical insulator and a wire main body. The electrical insulator encircles the wire main body, so that a first terminal and a second terminal are exposed from the wire main body, and the first terminal is connected to one of the two connecting members. Each of the electrically connecting components includes a connecting portion, to which the second terminal of the wire main body is fixed.

Abstract: A system for image scaling and enhancement is provided. The system includes a scaling processing unit, a deep-learning residue network unit and a combination unit. The filter scaling processing unit is configured to upscale a low-resolution image to output a high-resolution image. The deep-learning residue network unit is operated based on a deep-learning result, and configured to output a high-resolution residue image corresponding to the low-resolution image. The combination unit is configured to adjust the high-resolution residue image according to a weighting factor and combine an adjusted high-resolution residue image and the high-resolution image, in order to output an enhanced image, wherein the weighting factor is different from a reference weighting factor being used in a deep-learning procedure for training the deep-learning residue network unit.

Abstract: A Lamb wave resonator includes a piezoelectric material layer, a first finger electrode, a second finger electrode, at least two floating electrodes, and at least two gaps. The first finger electrode is disposed on one side of the piezoelectric material layer and includes a first main portion and first fingers. The second finger electrode is disposed on the side of the piezoelectric material layer and includes a second main portion and second fingers. The first fingers are parallel to and alternately arranged with the second fingers. The floating electrodes are disposed between each first finger and each second finger, and the gaps are disposed at two ends of each floating electrode, respectively.

Abstract: A traffic condition prediction system and a traffic condition prediction method are disclosed. The method includes: determining a center of circle in a surveillance image of a target traffic scene; determining a first circle based on the center of circle and a first radius; extracting a plurality of first feature points along the circumference of the first circle according to a first preset sampling frequency; generating a scene feature of the target traffic scene at least based on the first feature points; determining whether the scene feature and a scene feature of another traffic scene are similar; and when determining that they are similar, predicting traffic condition of the target traffic scene through a prediction model used for predicting traffic condition of the other traffic scene. The scene feature of the target traffic scene and that of the other traffic scene are generated in a same way.

Abstract: The present invention provides an object position determination circuit including a receiving circuit and a detecting circuit. In operations of the object position determination circuit, the receiving circuit receives an image signal; and the detecting circuit detects a position of an object in an Nth frame of the image signal, determines a partial region within an (N+M)th frame of the image signal according to the position of the object in the Nth frame, and only detects the partial region within the (N+M)th frame to determine a position of the object in the (N+M)th frame, wherein N and M are positive integers.

Abstract: A lighting ventilation fan is disclosure and includes a housing, a grille support, a fan module, a lighting base, plural connecting columns, a lighting module and a function module. The grille support, the lighting base and the lighting module form a grille structure mounted under a ceiling. The fan module disposed within the housing forms an airflow flowing. The lighting base and the grille support are connected through the plural connecting columns and spaced apart from each other. An inlet radially formed between the lighting base and the grille support is in communication with the outlet through the grille opening. The lighting base includes a holder having a holding opening axially downward and faced away from the housing. The lighting module is disposed in the lighting base and has a light-emitting surface emitting light axially and radially. The function module is disposed within the holder through the holding opening.

Abstract: The present disclosure provides a plug connector adapted to insert into a receptacle connector, the receptacle connector includes a cage having an opening. The plug connector includes a metal shell and a conductive elastic member. The metal shell is provided with a recessed groove. The conductive elastic member includes a conductive elastic body, the conductive elastic body has a holding portion held in the recessed groove and a first electromagnetic shielding portion extending out of the recessed groove from the holding portion, when the plug connector is inserted forwardly into the receptacle connector, the first electromagnetic shielding portion enters into the opening of the cage of the receptacle connector and mechanically and electrically contacts the cage and the metal shell. However, some embodiments may not include a holding portion or recessed groove.

Abstract: A computing device divides a training image into a plurality of training blocks, and the training image includes a training object. The computing device calculates, for each of the training blocks, a correct confidence score of the training object covering the training block according to an image-marking data and a confidence-score-translating function, and the image-marking data includes a piece of location information of the training object in the training image. Then, the computing device trains a deep-learning model with the training image, the correct confidence scores and the image-marking data to generate the object-detecting model.

Abstract: A traffic condition prediction system and a traffic condition prediction method are disclosed. The method includes: determining a center of circle in a surveillance image of a target traffic scene; determining a first circle based on the center of circle and a first radius; extracting a plurality of first feature points along the circumference of the first circle according to a first preset sampling frequency; generating a scene feature of the target traffic scene at least based on the first feature points; determining whether the scene feature and a scene feature of another traffic scene are similar; and when determining that they are similar, predicting traffic condition of the target traffic scene through a prediction model used for predicting traffic condition of the other traffic scene. The scene feature of the target traffic scene and that of the other traffic scene are generated in a same way.

Abstract: Systems and methods are described for implementing a framework for testing on-demand code execution that is configured to be executed on coordinated devices. The testing framework allows the execution of tasks in a network based on-demand system. The resulting generation of an I/O file for accessing local resources can be intercepted and either processed via a handler process or transmitted to the local resource.

Abstract: A system for image scaling and enhancement is provided. The system includes a scaling processing unit, a deep-learning residue network unit and a combination unit. The filter scaling processing unit is configured to upscale a low-resolution image to output a high-resolution image. The deep-learning residue network unit is operated based on a deep-learning result, and configured to output a high-resolution residue image corresponding to the low-resolution image. The combination unit is configured to adjust the high-resolution residue image according to a weighting factor and combine an adjusted high-resolution residue image and the high-resolution image, in order to output an enhanced image, wherein the weighting factor is different from a reference weighting factor being used in a deep-learning procedure for training the deep-learning residue network unit.

Abstract: A feature determination apparatus and method adapted to multiple object sizes are provided. The apparatus individually supplies each of the object images to a convolution neural network having several convolution layers to generate multiple feature maps corresponding to each object image. The apparatus calculates a feature amount of each feature image of each object image. The apparatus determines an invalid layer start number of each object image according to a preset threshold and the feature amount corresponding to each object image. The apparatus determines a feature map extraction recommendation for each of a plurality of object sizes according to a size of each object image and the invalid layer start number of each object image.

Abstract: A computing device divides a training image into a plurality of training blocks, and the training image includes a training object. The computing device calculates, for each of the training blocks, a correct confidence score of the training object covering the training block according to an image-marking data and a confidence-score-translating function, and the image-marking data includes a piece of location information of the training object in the training image. Then, the computing device trains a deep-learning model with the training image, the correct confidence scores and the image-marking data to generate the object-detecting model.

Abstract: The present disclosure provides a plug connector adapted to insert into a receptacle connector, the receptacle connector includes a cage having an opening. The plug connector includes a metal shell and a conductive elastic member. The metal shell is provided with a recessed groove. The conductive elastic member includes a conductive elastic body, the conductive elastic body has a holding portion held in the recessed groove and a first electromagnetic shielding portion extending out of the recessed groove from the holding portion, when the plug connector is inserted forwardly into the receptacle connector, the first electromagnetic shielding portion enters into the opening of the cage of the receptacle connector and mechanically and electrically contacts the cage and the metal shell. However, some embodiments may not include a holding portion or recessed groove.

Abstract: Systems and methods are described for implementing a coordinator within a coordinated environment, which environment includes set of coordinated devices managed by the coordinator. The coordinator can be provisioned with a set of tasks, each task corresponding to a segment of code that can be executed by the coordinator, such as to manage the coordinated devices. The coordinator can further be provisioned with event flow information designating a routing of inputs to the coordinator computing device to destinations, such as task executions or coordinated devices. On receiving input, the coordinator can reference the event flow information to pass the input to an appropriate destination.

Abstract: A method for detecting one or more objects adjacent to a vehicle includes capturing a image of an object adjacent to the vehicle; determining a driving area in the image; cutting the driving area to form an identification window; selecting an identification area in the identification window; accessing a plurality of object image data in a memory to compare the plurality of object image data with the identification area; and identifying a specific object in the identification area by the processor.

Abstract: A system for determining a quantitative accuracy of a test movement relative to a reference movement includes a display output device, a memory, and a processor operatively connected to the display output device and the memory. The memory stores motion capture data and programming instructions. The processor executes the programming instructions to determine a quantitative accuracy of the test movement relative to the reference movement. A method, executable by the processor, for determining the quantitative accuracy includes receiving, with the processor, motion capture data that includes the reference movement and the test movement. The motion data is split into individual movements, and the test movement is aligned with the reference movement. The processor computes a quantitative accuracy of the test movement relative to the reference movement, and generates, with the display output device, a visualization representative of the test movement. The computed accuracy is encoded into the visualization.

Abstract: The present disclosure provides an electrical connection apparatus which includes a receptacle and a plug. The receptacle includes a cage, a heat sink, a thermal interface material layer, and an elastic sheathing member. The heat sink is provided to the cage and has a base portion. The base portion has a lower surface, a first side edge close to the port, and a second side edge spaced apart from the first side edge. The heat sink further has two first convex portions positioned on the lower surface and adjacent to the first side edge and a first concave portion positioned on the lower surface and adjacent to the second side edge. The thermal interface material layer is provided to the lower surface of the base portion. The elastic sheathing member sheathes the heat sink and the cage. The plug includes a second convex portion positioned on an upper surface.

Abstract: A projection device including at least one projection module and at least one adjusting structure is provided. The projection module has at least one optical axis. The projection module is adapted to provide an image beam. The image beam is transmitted along a light transmission path to a projection target and forms a projection image. The adjusting structure is connected to the projection module. The adjusting structure is adapted to drive at least a part of the projection module to rotate along the optical axis such that the projection image rotates.

Abstract: A computing system includes a processing system with at least one processing unit. The processing system is configured to receive a depth map with a first boundary of an object. The processing system is configured to receive a color image that corresponds to the depth map. The color image includes a second boundary of the object. The processing system is configured to extract depth edge points of the first boundary from the depth map. The processing system is configured to identify target depth edge points on the depth map. The target depth edge points correspond to color edge points of the second boundary of the object in the color image. In addition, the processing system is configured to snap the depth edge points to the target depth edge points such that the depth map is enhanced with an object boundary for the object.