Abstract: A method and fixture for local softening of a hot stamped member is provided. A local region to be softened of a hot stamping blank is covered by a heat-insulating layer, and the heat-insulating layer is covered by a heating layer. After the hot stamping blank is fed into a heating furnace, a local temperature difference is generated, thereby forming a soft region. Then, the hot stamping blank having soft and hard regions is fed into a thermoforming machine so as to be machined into a hot stamped member. A hot stamping blank is subjected to local temperature control during heating to form a hot stamping blank having soft and hard regions, so that a hot stamped member that is locally softened can be formed in one step without the need for tailor welding. Therefore, die costs can be reduced, and the dimensional precision of obtained parts is high.

Abstract: Some embodiments of the disclosure describe a batch coating apparatus. In some examples, the batch coating apparatus includes a fixing mechanism, a liquid holding mechanism, a liquid injection mechanism and a control device. The fixing mechanism includes a plurality of fixing portions for fixing work-pieces to be coated. The liquid holding mechanism includes a plurality of liquid tanks arranged side by side. The liquid injection mechanism includes a liquid storage portion and an infusion portion. The control device is configured to control at least one of a relative movement between the fixing mechanism and the liquid holding mechanism and a relative movement between the liquid holding mechanism and the liquid injection mechanism. The control device is configured to control the liquid injection mechanism to supply the coating liquid via the infusion portion.

Abstract: A cloud resource recommendation method and a related device are provided. The cloud resource recommendation device obtains a requirement of a user on a cloud resource and obtains information about the cloud node, where the requirement of the user includes a quality of service requirement of the user on a routing path between a cloud node and an access device of the user, where the information includes quality of service information of a routing path between the cloud node and the access device of the user. The cloud resource recommendation device determines a cloud resource recommendation result based on the quality of service requirement and the quality of service information, where the cloud resource recommendation result includes a recommended cloud node, and quality of service information of at least one routing path between the recommended cloud node and the access device of the user meets the quality of service requirement.

Abstract: The technology of this application relates to a transmission control method and apparatus. The method includes obtaining traffic information of a first node at M sample moments, where M is a positive integer greater than or equal to 1, determining first traffic information of the first node at a first moment based on the traffic information of the first node at the M sample moments, where the first moment does not belong to the M sample moments, determining first rate-limited traffic based on the first traffic information, where the first rate-limited traffic indicates maximum traffic that is allowed to pass through a first queue of the first node at the first moment, and limiting traffic of the first queue at the first moment based on the first rate-limited traffic.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: A pulse current generation circuit (100) for neural stimulation includes an analogue signal receiving device (101) for receiving an analogue signal; an analogue-to-digital converter (102) for converting the analogue signal into a digital control signal; a current signal controller (103) for producing, according to the digital control signal, pulse current parameters for generating bidirectional pulse current signals; and a current generator (104) for generating, according to the pulse current parameters, bidirectional pulse current signals for neural stimulation, and the current generator can generate pulse currents of different precisions according to the pulse current parameters. In addition, the present invention further relates to a charge compensation circuit, a charge compensation method, and an implantable electrical retina stimulator using the pulse current generation circuit or the charge compensation circuit.

Abstract: A semiconductor structure and a fabrication method thereof. The semiconductor structure, includes: a substrate; and magnetic tunnel junctions on the substrate, that each magnetic tunnel junction of the magnetic tunnel junctions includes a first region and a second region adjacent to the first region, each magnetic tunnel junction includes a multilayered material including material layers stacked along a normal direction of the substrate, and the material layers of each magnetic tunnel junction include at least one material layer that is different in the first region and the second region. The storage capacity density of the semiconductor structure is high.

Abstract: A pulse current generation circuit (100) for neural stimulation includes an analogue signal receiving device (101) for receiving an analogue signal; an analogue-to-digital converter (102) for converting the analogue signal into a digital control signal; a current signal controller (103) for producing, according to the digital control signal, pulse current parameters for generating bidirectional pulse current signals; and a current generator (104) for generating, according to the pulse current parameters, bidirectional pulse current signals for neural stimulation, and the current generator can generate pulse currents of different precisions according to the pulse current parameters. In addition, the present invention further relates to a charge compensation circuit, a charge compensation method, and an implantable electrical retina stimulator using the pulse current generation circuit or the charge compensation circuit.

Abstract: A communication method, device, and system are disclosed, and pertain to the field of communication technologies. The method includes: After determining a node set including a node that supports segment routing, a controller may send a target segment identifier list to a source node, to indicate the source node to forward a packet of a target flow based on the target segment identifier list, where the source node of the target flow in a communication network belongs to the node set. According to the communication method provided in this application, the target segment identifier list can be sent to the source node of the target flow, to indicate a packet forwarding path of the target flow, thereby reducing network link congestion, and improving packet forwarding efficiency.

Abstract: Some embodiments of the disclosure provide a method for measuring lesion features of hypertensive retinopathy. In some examples, the method includes: acquiring a fundus image (s110); identifying an optic disc region of the fundus image and dividing the fundus image into at least three regions including a first region (c1), a second region (c2), and a third region (c3) on the basis of the optic disc region (b) (s120); performing artery and vein segmentation on the fundus image by a deep learning-based arteriovenous segmentation model to obtain the arteriovenous segmentation result (s130), the arteriovenous vessel annotation results including an artery annotation result (e1), a vein annotation result (e2), and a small vessel annotation result (e3); and measuring lesion features in the fundus image on the basis of the three regions (c1, c2, c3) and the arteriovenous segmentation result (s140).

Abstract: The technology of this application relates to a transmission control method and apparatus. The method includes obtaining traffic information of a first node at M sample moments, where M is a positive integer greater than or equal to 1, determining first traffic information of the first node at a first moment based on the traffic information of the first node at the M sample moments, where the first moment does not belong to the M sample moments, determining first rate-limited traffic based on the first traffic information, where the first rate-limited traffic indicates maximum traffic that is allowed to pass through a first queue of the first node at the first moment, and limiting traffic of the first queue at the first moment based on the first rate-limited traffic.

Abstract: Some embodiments of the disclosure provide a measuring method and measuring apparatus of a blood vessel diameter of a fundus image. In some examples, the measuring method include the following steps: acquiring a fundus image, generating a blood vessel segmentation image based on the fundus image, performing resolution enhancement on the blood vessel segmentation image, extracting blood vessel skeletons from the enhanced blood vessel segmentation image and performing fitting on the blood vessel skeletons to obtain a continuous blood vessel skeleton and a vessel diameter measurement direction of measurement pixel points, generating a blood vessel contour corresponding to the measurement pixel points based on the enhanced blood vessel segmentation image, and calculating a blood vessel diameter corresponding to the measurement pixel points based on a number of blood vessel pixel points in the blood vessel contour.

Abstract: A cell selection method, related to the communications field, includes a terminal device sending a first request to a network device, where the first request is used to request to select a serving cell for the terminal device in an energy efficiency first manner. The terminal device sends first information to the network device, where the first information is used by the network device to select a cell in a first communications system or a cell in a second communications system for the terminal device. The terminal device receives configuration information of a serving cell from the network device, where the serving cell is selected by the network device from the cell in the first communications system or the cell in the second communications system in a manner that energy efficiency of the terminal device is first.

Abstract: Some embodiments of the disclosure describe a batch coating apparatus. In some examples, the batch coating apparatus includes a fixing mechanism, a liquid holding mechanism, a liquid injection mechanism and a control device. The fixing mechanism includes a plurality of fixing portions for fixing work-pieces to be coated. The liquid holding mechanism includes a plurality of liquid tanks arranged side by side. The liquid injection mechanism includes a liquid storage portion and an infusion portion. The control device is configured to control at least one of a relative movement between the fixing mechanism and the liquid holding mechanism and a relative movement between the liquid holding mechanism and the liquid injection mechanism. The control device is configured to control the liquid injection mechanism to supply the coating liquid via the infusion portion.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: A method includes: determining, by the control device, that a site receives a first service; determining that a mapping wavelength of a first service is blocked on an original routing path, where the original routing path includes a first line board connected to a first local dimension, and a wavelength occupied by the first local dimension includes the mapping wavelength of the first service; and routing, by the control device, the first service to a second line board connected to a second local dimension, where the mapping wavelength of the first service is available in the second local dimension.

Abstract: A magnetic control device (30) for a capsule endoscope (10) including at least a first magnet (11) and an imaging device (12), and the capsule endoscope (10) is located in a tissue cavity (3). The magnetic control device (30) includes a second magnet (31) and a first induction coil (32) arranged around the second magnet (31). The magnetic control device (30) is configured to generate a driving force to the capsule endoscope (10) to move the capsule endoscope (10) from a first position (P1) in the tissue cavity (3) to a second position towards an opposite side in the tissue cavity (3); to control a variable magnetic field to decelerate the capsule endoscope (10) to a predetermined speed when the capsule endoscope (10) approaches the second position (P2); and to generate the variable magnetic field for the capsule endoscope (10).

Abstract: A decoding method performed by a receive end device is disclosed. The decoding method includes: receiving a first bit signal; performing level-M forward error correction (FEC) decoding on the first bit signal to obtain a second bit signal, where M is a positive integer greater than zero; checking the second bit signal to obtain a first check result; performing level-(M+1) FEC decoding on the second bit signal based on the first check result to obtain a third bit signal; and, upon determining that M+1 reaches a first preset threshold, performing data processing on the third bit signal to obtain a fourth bit signal, where the fourth bit signal is used by the receive end device to obtain service data transmitted by a transmit end device.

Abstract: The present disclosure relates to modulation methods. One example method includes performing distribution matching (DM) encoding on N1 first bits to obtain N2 first symbols, determining N4 to-be-phase-modulated symbols whose signal powers are equal to a preset signal power from the N2 first symbols, performing phase modulation on the N4 to-be-phase-modulated symbols based on N3 second bits to obtain N4 second symbols, performing binary labeling (BL) encoding on the N4 second symbols and N2-N4 first symbols to obtain N5 BL encoded output bits, performing forward error correction (FEC) encoding on the N5 BL encoded output bits to obtain N6 FEC redundant bits, and performing quadrature amplitude modulation (QAM) mapping based on the N6 FEC redundant bits and the N5 BL encoded output bits to obtain N2 target QAM signals.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.