Abstract: An optical sensing device is disclosed. The optical sensing device includes a sensing pixel, a driving circuit and a first light shielding layer. The sensing pixel includes a sensing circuit and a sensing element electrically connected to the sensing circuit. The driving circuit is electrically connected to the sensing circuit. The first light shielding layer includes at least one first opening corresponding to the sensing element, and the first light shielding layer is overlapped with the driving circuit in a top-view direction of the optical sensing device.

Abstract: A device is disclosed and includes an input stage circuit, a switching circuit, and a first latch circuit. The input stage circuit generates a first input signal having a first voltage and a second input signal based on a third input signal. The switching circuit operates in response to a first control signal, and adjusts a voltage level of a first data line according to the first input signal and a voltage level of a second data line according to the second input signal. The first latch circuit is coupled to the switching circuit by the first data line and the second data line. The first latch circuit latches a data in response to the first control signal and a second control signal, and adjusts the voltage level of the first data line based on a second voltage different from the first voltage.

Abstract: A latch formed from a memory cell includes a clock input terminal configured to receive a clock signal, complementary first and second data terminals, and a latch circuit. The latch circuit has first and second inverters. The first inverter has an input terminal coupled to the first data terminal, and the second inverter has an input terminal coupled to the second data terminal. A first pass gate transistor is coupled between an output terminal of the second inverter and the first data terminal. A second pass gate transistor is coupled between an output terminal of the first inverter and the second data terminal. The first and second pass gate transistors each have a gate terminal coupled to the clock input terminal. The input terminal of the first inverter is not directly connected to the output terminal of the second inverter, and the input terminal of the second inverter is not directly connected to the output terminal of the first inverter.

Abstract: A supervised training device and method for training a neural network, and a supervised classification method and device based on the neural network are provided. The training device includes a storage unit and a processor. The processor accesses and executes a sampling module, a labelling module and a training module in the storage unit. The sampling module samples a first image and a second image from a first dataset. The labelling module tags the first image in response to a first control instruction to generate a first tagged image, and generates label data according to the first tagged image and the second image. The training module trains the neural network according to the label data.

Abstract: An object positioning system and an object positioning method are provided. The object positioning system includes a sensing device, a storage device and a processing device. The sensing device collects point cloud data obtained from a scene including a target object. The processing device inputs surrounding area data centered on a key point and a preset feature descriptor to a neural network to calculate a scene feature descriptor of the scene. The processing device performs feature matching between the scene feature descriptor and the preset feature descriptor, and calculates a position of the target object in an actual space. The invention utilizes the feature extraction capability of the neural network to effectively improve the accuracy and stability of target object identification and positioning.

Abstract: An image recognition method and an unmanned aerial vehicle system are provided. A training image marked with a specified range is received, and a plurality of features are extracted from the training image through a basic model to obtain a feature map. Next, a frame selection is performed on each point on the feature map to obtain a plurality of initial detection frames, and a plurality of candidate regions are selected in the initial detection frames based on the specified range. Thereafter, the obtained candidate regions are classified to obtain a target block, feature data corresponding to the target block is extracted from the feature map, and a parameter of the basic model is adjusted based on the extracted feature data. In the disclosure, a higher-resolution image is achieved, time flexibility is provided, and accuracy of image recognition is thereby improved.

Abstract: A supervised training device and method for training a neural network, and a supervised classification method and device based on the neural network are provided. The training device includes a storage unit and a processor. The processor accesses and executes a sampling module, a labelling module and a training module in the storage unit. The sampling module samples a first image and a second image from a first dataset. The labelling module tags the first image in response to a first control instruction to generate a first tagged image, and generates label data according to the first tagged image and the second image. The training module trains the neural network according to the label data.

Abstract: A method for organizing an electric energy and a kinetic energy of an electric vehicle includes the following steps. A plurality of electro-motor modules having different grade of kinetic energy are provided. A plurality of battery modules having different grade of electric energy are provided. A plurality of electric vehicle performances are preset, and one of the battery modules on the electric vehicle is arranged in accordance with the electric vehicle performances. One of the electro-motor modules on the electric vehicle is arranged in accordance with the electric vehicle performances and the one of the battery modules.

Abstract: A method for organizing an electric energy and a kinetic energy of an electric vehicle includes the following steps. A plurality of electro-motor modules having different grade of kinetic energy are provided. A plurality of battery modules having different grade of electric energy are provided. A plurality of electric vehicle performances are preset, and one of the battery modules on the electric vehicle is arranged in accordance with the electric vehicle performances. One of the electro-motor modules on the electric vehicle is arranged in accordance with the electric vehicle performances and the one of the battery modules.

Abstract: A method for forming an U-shaped metal frame includes a preparing means: bend forming a straight tube in a continuous curve shape to be a billet including a spaced section and a first and a second side segments twisted toward opposite directions individually; a forming means: placing the billet into a cavity of a forming mold, closing two ends of the billet, and feeding a high-pressure fluid into the billet; a cutting means: cutting the billet into the first and the second pipes, the first pipe including a first connecting portion formed in a first cut position thereof, the second pipe including a second connecting portion formed in a second cut position thereof; a connecting means: twisting the first side segment and the second side segment toward the same direction, and connecting the first connecting portion and the second connecting portion together to obtain the U-shaped metal frame.