Abstract: Methods and semiconductor devices are provided. A method includes determining a location of a polyimide opening (PIO) corresponding to an under-bump metallization (UBM) feature in a die. The die includes a substrate and an interconnect structure over the substrate. The method also includes determining a location of a stacked via structure in the interconnect structure based on the location of the PIO. The method further includes forming, in the interconnect structure, the stacked via structure comprising at most three stacked contact vias at the location of the PIO.

Abstract: A semiconductor structure includes a functional die, a dummy die, a redistribution structure, a seal ring and an alignment mark. The dummy die is electrically isolated from the functional die. The redistribution structure is disposed over and electrically connected to the functional die. The seal ring is disposed over the dummy die. The alignment mark is between the seal ring and the redistribution structure, wherein the alignment mark is electrically isolated from the dummy die, the redistribution structure and the seal ring. The insulating layer encapsulates the functional die and the dummy die.

Abstract: A method of manufacturing a semiconductor device includes forming a first layer of a first planarizing material over a patterned surface of a substrate, forming a second layer of a second planarizing material over the first planarizing layer, crosslinking a portion of the first planarizing material and a portion of the second planarizing material, and removing a portion of the second planarizing material that is not crosslinked. In an embodiment, the method further includes forming a third layer of a third planarizing material over the second planarizing material after removing the portion of the second planarizing material that is not crosslinked. The third planarizing material can include a bottom anti-reflective coating or a spin-on carbon, and an acid or an acid generator. The first planarizing material can include a spin-on carbon, and an acid, a thermal acid generator or a photoacid generator.

Abstract: Provided is a neural-network-based classification method, including: generating, by a neural network, one or more score vectors corresponding to one or more samples respectively; determining a first subset of the one or more samples according to the one or more score vectors and a first decision threshold, wherein the first subset is associated with a first class; and selecting samples to be re-examined from the one or more samples according to the first subset.

Abstract: A personalized parameter learning method, a sleep-aid device and a non-transitory computer readable medium are provided. The personalized parameter learning method for a sleep-aid device is provided. The personalized parameter learning method includes the following steps. A process device computes a measured sleep quality of a user after operating a sleep-aid device with an inputted parameter setting at least according to a subjective feedback from the user. The processing device generates a plurality of candidate parameter settings according to the measured sleep quality. The processing device generates a plurality of predicting sleep qualities corresponding the candidate parameter settings. The processing device obtains a recommending parameter setting by selecting one of the candidate parameter settings according to the predicting sleep qualities.

Abstract: A personalized parameter learning method, a sleep-aid device and a non-transitory computer readable medium are provided. The personalized parameter learning method for a sleep-aid device is provided. The personalized parameter learning method includes the following steps. A process device computes a measured sleep quality of a user after operating a sleep-aid device with an inputted parameter setting at least according to a subjective feedback from the user. The processing device generates a plurality of candidate parameter settings according to the measured sleep quality. The processing device generates a plurality of predicting sleep qualities corresponding the candidate parameter settings. The processing device obtains a recommending parameter setting by selecting one of the candidate parameters according to the predicting sleep qualities.

Abstract: Provided is a neural-network-based classification method, including: generating, by a neural network, one or more score vectors corresponding to one or more samples respectively; determining a first subset of the one or more samples according to the one or more score vectors and a first decision threshold, wherein the first subset is associated with a first class; and selecting samples to be re-examined from the one or more samples according to the first subset.

Abstract: A deep neural network and a method for recognizing and classifying a multimedia data as one of a plurality of pre-determined data classes with enhanced recognition and classification accuracy and efficiency are provided. The use of the side branch(es) (or sub-side branch(es), sub-sub-side branch(es), and so on) extending from the main branch (or side branch(es), sub-side branch(es), and so on), the sequential decision making mechanism, and the collaborating (fusing) decision making mechanism in a deep neural network would equip a deep neural network with the capability for fast forward inference so as to enhance recognition and classification accuracy and efficiency of the deep neural network.

Abstract: A deep neural network and a method for recognizing and classifying a multimedia data as one of a plurality of pre-determined data classes with enhanced recognition and classification accuracy and efficiency are provided. The use of the side branch(es) (or sub-side branch(es), sub-sub-side branch(es), and so on) extending from the main branch (or side branch(es), sub-side branch(es), and so on), the sequential decision making mechanism, and the collaborating (fusing) decision making mechanism in a deep neural network would equip a deep neural network with the capability for fast forward inference so as to enhance recognition and classification accuracy and efficiency of the deep neural network.

Abstract: A traffic forecasting system configured to forecast a travel time of a route is disclosed. The traffic forecasting system includes a model-training module, a model-selecting module and a forecasting module. The model-training module builds multiple candidate models. Each of the candidate models corresponds to one of a plurality of road section and one of a plurality of mathematical models. The model-selecting module selects an estimated model corresponding to the road section from the candidate models matching the road sections of the route. The forecasting module calculates estimated vehicle speeds of each road section according to the estimated models of each road section of the route. The model-selecting module selects one of the candidate models corresponding to the road section with the smallest error as the estimated model of the road section.

Abstract: A positioning control method, suitable for an electronic device moving along with a vehicle, is provided in this disclosure. The electronic device includes an image-capturing module and a satellite-based positioning module. The positioning control method includes steps of: capturing a first image; extracting an icon object from the first image; capturing a second image; identifying the corresponding icon object in the second image; calculating a separation distance between the electronic device and the icon object according to a variance of the icon object between the first and the second images and a displacement distance of the vehicle; searching a point-of-interest (POI) corresponding to the icon object from a POI database; and, calculating/calibrating positioning data of the electronic device according to the separation distance and known coordinates of the POI.

Abstract: An image processing device and an image processing method thereof are provided. The image processing device uses feature points of an image to calculate weights of endpoints of straight lines in the image, and determines coordinates of the endpoints of the straight lines after image morphing. The image processing device further uses the transformed coordinates of the endpoints of the straight lines to calculate corresponding coordinates of points of the straight lines. The image processing device further uses orthogonal relation between coordinates of image points and the coordinates of points of the straight lines to add feature points, and calculates coordinates of the image points after image morphing.

Abstract: An image processing device and an image processing method thereof are provided. The image processing device uses feature points of an image to calculate weights of endpoints of straight lines in the image, and determines coordinates of the endpoints of the straight lines after image morphing. The image processing device further uses the transformed coordinates of the endpoints of the straight lines to calculate corresponding coordinates of points of the straight lines. The image processing device further uses orthogonal relation between coordinates of image points and the coordinates of points of the straight lines to add feature points, and calculates coordinates of the image points after image morphing.

Abstract: A positioning control method, suitable for an electronic device moving along with a vehicle, is provided in this disclosure. The electronic device includes an image-capturing module and a satellite-based positioning module. The positioning control method includes steps of: capturing a first image; extracting an icon object from the first image; capturing a second image; identifying the corresponding icon object in the second image; calculating a separation distance between the electronic device and the icon object according to a variance of the icon object between the first and the second images and a displacement distance of the vehicle; searching a point-of-interest (POI) corresponding to the icon object from a POI database; and, calculating/calibrating positioning data of the electronic device according to the separation distance and known coordinates of the POI.

Abstract: An image processing apparatus and an image processing method are provided. A storage unit of the image processing apparatus stores an image and a piece of intensity statistical information of the image. The piece of intensity statistical information records that the image has a first number of pixels having a first intensity value, a second number of pixels having a second intensity value, and a third number of pixels having a third intensity value. A micro-processing unit of the image processing unit calculates a centralization degree of the second intensity value in the image according to the first number, the second number, and the third number. If the centralization degree is greater than a threshold, the micro-processing unit adjusts the intensity value of a pixel having the second intensity value in the image according to the second intensity value and the intensity value of the at least one neighboring pixel of the pixel.

Abstract: A real-time navigation electronic device based on determining current traffic rule information includes a positioning unit, a user interface, an information detecting-and-generating unit and a processing unit. The information detecting-and-generating unit detects and generates information for determining real-time traffic rule. The processing unit generates a navigation route according to the destination and the present position. The processing unit receives the information for determining real-time traffic rule. The processing unit determines if the information for determining real-time traffic rule conflicts with the navigation route or makes the navigation route not the quickest route. The processing unit re-generates the navigation route according to the destination, the present position and the information for determining real-time traffic rule when the information for determining real-time traffic rule conflicts with the navigation route or makes the navigation route not the quickest route.

Abstract: First, the image recognition method of the present invention transforms a first Cartesian coordinate value of a first image and a second Cartesian coordinate value of a second image in the Cartesian coordinate system into a first polar coordinate value and a second polar coordinate value in a polar coordinate system, respectively. Afterwards, the image recognition method adjusts the first image and the second image to multiple scales based on a radial coordinate of the polar coordinate system, and obtains a plurality of first local description values and a plurality of second local description values by analyzing the first interest points of the first image and the second interest points of the second image on the multiple scales, respectively. Finally, by intercomparing the first local description values and the second local description values, a matching feature between the first image and the second image is recognized.

Abstract: A real-time navigation electronic device based on determining current traffic rule information includes a positioning unit, a user interface, an information detecting-and-generating unit and a processing unit. The information detecting-and-generating unit detects and generates information for determining real-time traffic rule. The processing unit generates a navigation route according to the destination and the present position. The processing unit receives the information for determining real-time traffic rule. The processing unit determines if the information for determining real-time traffic rule conflicts with the navigation route or makes the navigation route not the best route. The processing unit re-generates the navigation route according to the destination, the present position and the information for determining real-time traffic rule when the information for determining real-time traffic rule conflicts with the navigation route or makes the navigation route not the best route.

Abstract: An image processing apparatus and an image processing method are provided. A storage unit of the image processing apparatus stores an image and a piece of intensity statistical information of the image. The piece of intensity statistical information records that the image has a first number of pixels having a first intensity value, a second number of pixels having a second intensity value, and a third number of pixels having a third intensity value. A micro-processing unit of the image processing unit calculates a centralization degree of the second intensity value in the image according to the first number, the second number, and the third number. If the centralization degree is greater than a threshold, the micro-processing unit adjusts the intensity value of a pixel having the second intensity value in the image according to the second intensity value and the intensity value of the at least one neighboring pixel of the pixel.

Abstract: First, the image recognition method of the present invention transforms a first Cartesian coordinate value of a first image and a second Cartesian coordinate value of a second image in the Cartesian coordinate system into a first polar coordinate value and a second polar coordinate value in a polar coordinate system, respectively. Afterwards, the image recognition method adjusts the first image and the second image to multiple scales based on a radial coordinate of the polar coordinate system, and obtains a plurality of first local description values and a plurality of second local description values by analyzing the first interest points of the first image and the second interest points of the second image on the multiple scales, respectively. Finally, by intercomparing the first local description values and the second local description values, a matching feature between the first image and the second image is recognized.