Abstract: An embodiment composite material for semiconductor package mount applications may include a first component including a tin-silver-copper alloy and a second component including a tin-bismuth alloy or a tin-indium alloy. The composite material may form a reflowed bonding material having a room temperature tensile strength in a range from 80 MPa to 100 MPa when subjected to a reflow process. The reflowed bonding material may include a weight fraction of bismuth that is in a range from approximately 4% to approximately 15%. The reflowed bonding material may an alloy that is solid solution strengthened by a presence of bismuth or indium that is dissolved within the reflowed bonding material or a solid solution phase that includes a minor component of bismuth dissolved within a major component of tin. In some embodiments, the reflowed bonding material may include intermetallic compounds formed as precipitates such as Ag3Sn and/or Cu6Sn5.

Abstract: A vibration-isolating device based on magnetic damping includes a housing and an internal seat. The housing receives a first magnetic member and has a first buffer. The internal seat has a base portion received in the housing and has a raised portion protruding from the housing. The base portion has a second magnetic member positionally opposite to the first magnetic member to generate a damping effect. A second buffer is between the base portion and the housing and aligned with the first buffer. When an object is fixed to the raised portion, the object and the housing jointly hold the first buffer and leave a gap therebetween. The housing and the base portion hold the second buffer therebetween. When the object receives vibration, the first buffer and the second buffer damp the vibration first and the internal seat uses the damping effect to counteract any remaining part of the vibration.

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: A vibration-isolating device based on magnetic damping includes a housing and an internal seat. The housing receives a first magnetic member and has a first buffer. The internal seat has a base portion received in the housing and has a raised portion protruding from the housing. The base portion has a second magnetic member positionally opposite to the first magnetic member to generate a damping effect. A second buffer is between the base portion and the housing and aligned with the first buffer. When an object is fixed to the raised portion, the object and the housing jointly hold the first buffer and leave a gap therebetween. The housing and the base portion hold the second buffer therebetween. When the object receives vibration, the first buffer and the second buffer damp the vibration first and the internal seat uses the damping effect to counteract any remaining part of the vibration.

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: 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: 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 vehicle detecting detection assisting method and a vehicle detection assisting system are provided. The vehicle detection assisting method includes the following steps. A scanning range of a lidar unit is obtained. A width of a lane is obtained. A trace of the lane is obtained. A dynamic region of interest in the scanning range is created according to the width and the trace.

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 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: The present invention relates to a microelectromechanical microphone package structure which includes a substrate, a metallic cover, an application-specific integrated circuit and a microphone chip. The metallic cover caps the substrate, and a chamber is formed between the metallic cover and the substrate. The application-specific integrated circuit is disposed in the chamber and electrically connected with the substrate. The microphone chip is disposed in the chamber and electrically connected with the application-specific integrated circuit. A filter is disposed between the substrate and the microphone chip and located correspondingly to a sound hole of the substrate, so as to effectively prevent external objects or mists from getting into the chamber through the sound hole, thereby attaining the effect of protecting the microphone chip.

Abstract: An image annotation method includes the following. Image frames are acquired. One or more target objects are identified and tracked from the image frames. Candidate key image frames are selected from the frames according to a first selection condition. First similarity indexes of the candidate key image frames are determined. Second similarity indexes of a plurality of adjacent frames are determined. The candidate key image frames as well as the adjacent frames that meet a second selection condition are selected as key image frames. The key image frames are displayed on a graphical user interface. Annotation information about the one or more target objects is display through the graphical user interface.

Abstract: A substrate includes a substrate body and an interconnection layer disposed on a bearing surface of the substrate body and having an annular portion and a plurality of protrusions extending outward from an outer periphery of the annular portion. A package module is formed by the substrate, a chip mounted on the bearing surface of the substrate body, and a cap enclosing the chip and having a bottom thereof adhered to the interconnection layer of the substrate by an adhesive. By means of the protrusions of the interconnection layer, the bonding area of the adhesive is increased and the spread of the adhesive is effectively concentrated.

Abstract: An apparatus for monitoring an object in a low light environment includes an image capturing unit, a vehicle speed unit, an image recognizing unit, and an object determining unit. The image capturing unit continuously captures and outputs time-sliced images. The vehicle speed unit detects and outputs current vehicle speed information. The image recognizing unit recognizes a region having pixel brightness higher than a threshold in each of the time-sliced images and marks the region as a high brightness block. The object determining unit selects at least two successive time-sliced images having high brightness blocks in a continuous corresponding variation relationship from the time-sliced images, generates and outputs estimated speed information, and when the estimated speed information is different from the current vehicle speed information, determines that the high brightness block is a moving object block and monitors the moving object block.

Abstract: A substrate includes a substrate body and an interconnection layer disposed on a bearing surface of the substrate body and having an annular portion and a plurality of protrusions extending outward from an outer periphery of the annular portion. A package module is formed by the substrate, a chip mounted on the bearing surface of the substrate body, and a cap enclosing the chip and having a bottom thereof adhered to the interconnection layer of the substrate by an adhesive. By means of the protrusions of the interconnection layer, the bonding area of the adhesive is increased and the spread of the adhesive is effectively concentrated.

Abstract: An image annotation method includes the following. Image frames are acquired. One or more target objects are identified and tracked from the image frames. Candidate key image frames are selected from the frames according to a first selection condition. First similarity indexes of the candidate key image frames are determined. Second similarity indexes of a plurality of adjacent frames are determined. The candidate key image frames as well as the adjacent frames that meet a second selection condition are selected as key image frames. The key image frames are displayed on a graphical user interface. Annotation information about the one or more target objects is display through the graphical user interface.

Abstract: A vehicle detecting method and a vehicle detecting system are provided. The vehicle detecting method includes the following steps. A scanning range of a lidar unit is obtained. A width of a lane is obtained. A trace of the lane is obtained. A dynamic region of interest in the scanning range is created according to the width and the trace.

Abstract: A system of detection, tracking and identification of an evolutionary adaptation of a vehicle lamp includes an image capture device and a processor. The image capture device captures an image of a vehicle. The processor processes the image of the vehicle to generate a detection result of the vehicle lamp, analyzes and integrates vehicle lamp dynamic motion information and vehicle lamp multiple scale variation information based on the detection result, and then tracks the position of the vehicle lamp by applying a multiple scale vehicle lamp measurement model.