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.

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 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 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: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal 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 diagnosis result.

Abstract: A stent is disclosed, which includes a stent body (2) and a single-radiopaque component (1) disposed at one or each of a proximal end and a distal end of the stent body (2). The stent body (2) is composed of rings and struts, and one part of the single-radiopaque component (1) is received in a receptacle (3) of the stent body (2) and another part of the single-radiopaque component (1) protrudes out of a surface of the stent body (2). The area of the protruding part (11) of the single-radiopaque component (1) is larger than an area of the embedded part (10), the presence of the protruding part (11) allowing the single-radiopaque component (1) to appear wider and thicker in a radiologic image, enhancing the radiopacity of the stent during surgery.

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 stent is disclosed, which includes a stent body and a single-radiopaque component disposed at one or each of a proximal end and a distal end of the stent body. The stent body is composed of rings and struts, and one part of the single-radiopaque component is received in a receptacle of the stent body and another part of the single-radiopaque component protrudes out of a surface of the stent body. The area of the protruding part of the single-radiopaque component is larger than an area of the embedded part, the presence of the protruding part allowing the single-radiopaque component to appear wider and thicker in a radiologic image, enhancing the radiopacity of the stent during surgery.

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: 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: 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 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: The present disclosure provides a method for identifying a clique network using a Pregel graph computing framework. The method includes determining, according to a data transfer feature of a target clique, a transmitting direction of attribute values of nodes; transmitting, an edge vector of the edge and an attribute value of the first node to the second node along the transmitting direction in a constructed data transmission relationship network, the edge vector comprising a plurality of data transfer eigenvalues; performing, weighted calculation on the edge vector received by the second node to obtain an optimal weighted edge; iterating, the above operations; and determining, according to attribute values of the nodes after the one or more iterations, nodes in the target clique, and determining attributes of the nodes in the target clique.

Abstract: The present disclosure provides a method for identifying a clique network using a Pregel graph computing framework. The method includes determining, according to a data transfer feature of a target clique, a transmitting direction of attribute values of nodes; transmitting, an edge vector of the edge and an attribute value of the first node to the second node along the transmitting direction in a constructed data transmission relationship network, the edge vector comprising a plurality of data transfer eigenvalues; performing, weighted calculation on the edge vector received by the second node to obtain an optimal weighted edge; iterating, the above operations; and determining, according to attribute values of the nodes after the one or more iterations, nodes in the target clique, and determining attributes of the nodes in the target clique.

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 stent is disclosed, which includes a stent body (2) and a single-radiopaque component (1) disposed at one or each of a proximal end and a distal end of the stent body (2). The stent body (2) is composed of rings and struts, and one part of the single-radiopaque component (1) is received in a receptacle (3) of the stent body (2) and another part of the single-radiopaque component (1) protrudes out of a surface of the stent body (2). The area of the protruding part (11) of the single-radiopaque component (1) is larger than an area of the embedded part (10), the presence of the protruding part (11) allowing the single-radiopaque component (1) to appear wider and thicker in a radiologic image, enhancing the radiopacity of the stent during surgery.

Abstract: An apparatus for segmenting and feeding a fiber includes at least one capillary having a lumen therethrough configured to deliver a fiber segment. An advancing advances a fiber through the capillary. Tensioning means applies tension to the fiber to induce it to break. Damaging means locally damages the fiber.