Abstract: This application discloses a power supply circuit, a driving apparatus, and a display apparatus, and relates to the field of display technologies. The power supply circuit includes: a first power supply module and a second power supply module. An output end of the first power supply module is connected to a first output end of the second power supply module, and is configured to be connected to a first input end of the driving module. An output end of the first power supply module is configured to output a first voltage signal, and a first output end of the second power supply module is configured to output a second voltage signal. A second output end of the second power supply module is configured to be connected to a second input end of the driving module, so as to output a third voltage signal.

Abstract: A display substrate (20) and a display device (40). The display substrate (20) comprises a base substrate and a plurality of sub-pixels (100) located on the base substrate. Each sub-pixel (100) comprises a pixel circuit and a pixel electrode (134) electrically connected to the pixel circuit, and each pixel circuit comprises a driving sub-circuit. The pixel electrodes (134) each comprises an electrode body part (140) and a first electrode extension part (142) extending from the electrode body part (140). The display substrate (20) comprises first-type sub-pixels. The electrode body part (140) of each first-type sub-pixel (100) does not overlap a control electrode of the driving sub-circuit of the sub-pixel or an electrode part directly electrically connected to the control electrode, and the first electrode extension part (142) of each pixel electrode (134) at least partially overlaps the control electrode or the electrode part.

Abstract: A multi-viewpoint image processing system includes a processing apparatus and a display apparatus, the processing apparatus includes an acquisition module and an encoding module, wherein the acquisition module is configured to acquire K viewpoint images, and a viewpoint image includes M rows and N columns of pixels; the encoding module is configured to receive the K viewpoint images, encode the K viewpoint images to generate a plurality of encoded images, and send the plurality of encoded images to the display apparatus; the display apparatus is configured to receive the plurality of encoded images and obtain M pieces of display information according to the plurality of encoded images, an i-th piece of display information including i-th rows of pixels of the K viewpoint images, i=1, 2, . . . , M; and perform stereoscopic display according to the M pieces of display information.

Abstract: A display method comprises: acquiring multiple frames of images to be displayed, one frame of two adjacent frames of the images to be displayed being an image in an odd-numbered row, the other frame being an image in an even-numbered row, the resolution of the image in the odd-numbered row being M*(a*N), the resolution of the image in the even-numbered row being M*(a*N) or (M?1)*(a*N), and a being 1 or 2; and causing an odd-numbered display group to display the image in the odd-numbered row, and causing an even-numbered display group to display the image in the even-numbered row, the odd-numbered display group comprising M*(a*N) odd-numbered display units, the even-numbered display group comprising (M?1)*(a*N) even-numbered display units, the odd-numbered display unit in the i-th row comprising a first sub-pixel, a second sub-pixel, and a third sub-pixel in a pixel unit in the i-th row.

Abstract: Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to a robotic device for picking items from a bin and perturbing items in a bin. The robotic device may include one or more picking elements and one or more perturbation elements for disturbing a present arrangement of items in the bin. In an exemplary embodiment, a perturbation element comprises a compressed air valve. In some implementations, the robotic device may also include one or more computer-vision systems. Based on image data from the one or more computer-vision systems, a strategy for picking up items from the bin is determined. When no strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points.

Abstract: Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to an artificial neural network training method that does not require extensive training data or time expenditure. The few-shot training model disclosed herein includes attempting to pick up items and, in response to a failed pick up attempt, transferring and generalizing information to similar regions to improve probability of success in future attempts. In some implementations, the training method is used to robotic device for picking items from a bin and perturbing items in a bin. When no picking strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points. In some implementations, the device may include one or more Computer-vision systems.

Abstract: Various embodiments of the present technology generally relate to robotic devices, artificial intelligence, and computer vision. More specifically, some embodiments relate to an imaging process for detecting failure modes in a robotic motion environment. In one embodiment, a method of detecting failure modes in a robotic motion environment comprises collecting one or more images of a multiple scenes throughout a robotic motion cycle. Images may be collected by one or more cameras positioned at one or more locations for collecting images with various views. Images collected throughout the robotic motion cycle may be processed in real-time to determine if any failure modes are present in their respective scenes, report when failure modes are present, and may be used to direct a robotic device accordingly.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free, and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to a robotic device for picking items from a bin and perturbing items in a bin. In some implementations, the device may include one or more computer-vision systems. A computer-vision system, in accordance with the present technology, may use at least two two-dimensional images to generate three-dimensional (3D) information about the bin and items in the bin. Based on the 3D information, a strategy for picking up items from the bin is determined. When no strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points and re-attempt to pick up an item.

Abstract: Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to an artificial neural network training method that does not require extensive training data or time expenditure. The few-shot training model disclosed herein includes attempting to pick up items and, in response to a failed pick up attempt, transferring and generalizing information to similar regions to improve probability of success in future attempts. In some implementations, the training method is used to robotic device for picking items from a bin and perturbing items in a bin. When no picking strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points. In some implementations, the device may include one or more Computer-vision systems.

Abstract: Various embodiments of the present technology generally relate to robotic devices, computer-vision systems, and artificial intelligence. More specifically, some embodiments relate to a computer-vision system for robotic devices. In some implementations, a computer-vision is coupled to a robotic arm for picking items from a bin and perturbing items in a bin. A computer-vision system, in accordance with the present technology, may use at least two two-dimensional (2D) images to generate three-dimensional (3D) information about the bin and items in the bin. A method of training artificial neural networks to generate 3D information from 2D images includes obtaining ground truth data for a scene, obtaining at least two images of the scene, and providing the ground truth data and the at least two images to the artificial neural network configured to generate a depth map from the images based on the ground truth data.

Abstract: Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to a robotic device for picking items from a bin and perturbing items in a bin. The robotic device may include one or more picking elements and one or more perturbation elements for disturbing a present arrangement of items in the bin. In an exemplary embodiment, a perturbation element comprises a compressed air valve. In some implementations, the robotic device may also include one or more computer-vision systems. Based on image data from the one or more computer-vision systems, a strategy for picking up items from the bin is determined. When no strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points.

Abstract: An apparatus, comprising a first sampling circuit configured to sample a clock signal according to a data signal to produce a first sampled signal, a second sampling circuit configured to sample the clock signal according to a delay signal to produce a second sampled signal, and a control circuit coupled to the first sampling circuit and the second sampling circuit, wherein the control circuit is configured to perform a not-and (NAND) operation according to the first sampled signal and the second sampled signal to produce an activation signal for activating a frequency adjustment for the clock signal.

Abstract: A multiplexer comprises: an output circuit comprising a multiplexer output; and a first buffer coupled to the output circuit and comprising: a first selection input configured to receive a first selection signal; a first logical input configured to receive a first logical input signal; and a first ground; wherein the multiplexer is configured to: couple the first logical input to the multiplexer output when the first selection signal is a first value; and couple the first logical input to the first ground when the first selection signal is a second value. A method comprises: receiving a selection signal and a first logical input signal; coupling a first logical input to a multiplexer output when the selection signal is a first value; and coupling the first logical input to a ground when the selection signal is a second value.

Abstract: An apparatus comprising an input port configured to receive an input signal propagated through a transmission link, wherein the transmission link comprises a low-frequency channel loss and a high-frequency channel loss, a continuous-time linear equalization (CTLE) circuit coupled to the input port and configured to produce an output signal according to the input signal by applying a first gain to the input signal at a first frequency to compensate the low-frequency loss, and applying a second gain to the input signal at a second frequency to compensate the high-frequency channel loss, and an output port coupled to the CTLE circuit and configured to output the output signal.

Abstract: An apparatus comprising a semiconductor chip that comprises an optical modulator configured to modulate an optical signal based on a received driver signal, a voltage-mode (VM) driver coupled to the optical modulator and configured to produce a level-shifted driver signal to modulate the optical signal, and a two-stage test interface coupled to the optical modulator and configured to receive and test the level shifted driver signal. The two-stage test interface comprises a voltage equalization stage coupled to an output-terminated buffer stage, the VM driver comprises a two-stage VM Mach-Zehnder modulator (MZM) driver that comprises a pre-driver coupled to a VM level-shifter (VMLS). The apparatus further comprises a resistor coupled to an output of the buffer stage, wherein the resistor comprises an amount of resistance that matches a termination resistance of a test equipment. The termination resistance is about 50 ohm (?).