Abstract: Disclosed is a method comprising providing to a machine learning model, as an input, a channel estimation or a plurality of raw pilots, obtaining, from the machine learning model, as an output, a label indicating a channel model, wherein the channel model is representative of channel conditions, based on the label, determining a waveform associated with the channel conditions, indicating the waveform to the receiver, and performing the transmission to the receiver using the waveform.

Abstract: A method of operation of a compute system includes: generating a digital model from a input image; segmenting a 3D-polygon from the input image by applying a triangular mesh; assembling a 3D surface, based on the digital model of a zone of interest from the 3D-polygon; calculating an average threshold value of pixels within the zone of interest for identifying a condition over the 3D surface; and assembling a skin condition display from analyzing by a skin artificial intelligence (AI) of the 3D surface for displaying on a device.

Abstract: A method of operation of a compute system includes: detecting a skin area in a patient image; segmenting the skin area into a segmented image having an acne pimple at the center; generating a target pixel array from the segmented image includes separating a plurality of the acne pimples that are adjacent in the segmented image; identifying an acne characterization of the acne pimples including an area of each acne and an acne score; and assembling a user interface display from the acne characterization for displaying on a device.

Abstract: A method of operation of a compute system includes: verifying a patient image as an acceptable image; detecting a body part from the acceptable image; generating a skin segmentation for the body part including a non-skin region and a skin region based on the acceptable image; and generating a local Hidradenitis Suppurativa (HS) severity score based on the patient image for displaying on a device to assist in diagnosis.

Abstract: A method of operation of a compute system includes: receiving a patient image; segmenting a skin lesion in the patient image; constructing a normalized image by cropping the patient image and adding padding to position the skin lesion at the center of the normalized image; identifying, by a cancer artificial intelligence (AI) already trained, a skin cancer classification, a skin cancer sub-class, and a risk level assessment; and generating a skin cancer display including the normalized image, the skin cancer classification, the skin cancer sub-class, and the risk level assessment for displaying on a device.

Abstract: A method of operation of a compute system includes: generating a skin segmentation including a non-skin region and a skin prediction based on a patient image; generating a body part segmentation based on the patient image; generating a cropped image based on the skin segmentation and the body part segmentation with the cropped image includes the non-skin region based on the skin prediction; generating a eczema segmentation based on the cropped image and the skin prediction; generating intermediate scores for erythema, papulation, lichenification, and excoriation for the cropped image; and generating a full body EASI score based on the eczema segmentation and the intermediate scores for displaying on a device to assist in diagnosis.

Abstract: A method of operation of a compute system includes: generating a skin segmentation including a non-skin region and a skin prediction based on a patient image; generating a body part segmentation based on the patient image; generating a cropped image based on the skin segmentation and the body part segmentation with the cropped image includes the non-skin region based on the skin prediction; generating a lesion segmentation based on the cropped image and the skin prediction; generating intermediate scores for erythema, induration, desquamation for the cropped image; and generating a full body severity score based on the lesion segmentation and the intermediate scores for displaying on a device to assist in diagnosis.

Abstract: A method of operation of a compute system comprising: detecting a skin area in a patient image; generating a normalized image by applying a rescale factor to the skin area; cropping a target pixel array from the normalized image with an acne pimple at the center of the target pixel array; generating an acne type by analyzing the target pixel array including calculating an acne severity score and calculating an area of each acne; and generating a user interface display including an acne indication and the acne severity score for displaying on a device.

Abstract: A method of operation of a compute system includes: detecting a skin area in a patient image; segmenting the skin area into a segmented image having an acne pimple at the center; generating a target pixel array from the segmented image includes separating a plurality of the acne pimples that are adjacent in the segmented image; identifying an acne characterization of the acne pimples including an area of each acne and an acne score; and assembling a user interface display from the acne characterization for displaying on a device.

Abstract: A method of operation of a compute system includes: verifying a patient image as an acceptable image; detecting a body part from the acceptable image; generating a skin segmentation for the body part including a non-skin region and a skin region based on the acceptable image; and generating a local Hidradenitis Suppurativa (HS) severity score based on the patient image for displaying on a device to assist in diagnosis.

Abstract: A method of operation of a compute system includes: generating a skin segmentation including a non-skin region and a skin prediction based on a patient image; generating a body part segmentation based on the patient image; generating a cropped image based on the skin segmentation and the body part segmentation with the cropped image includes the non-skin region based on the skin prediction; generating a eczema segmentation based on the cropped image and the skin prediction; generating intermediate scores for erythema, papulation, lichenification, and excoriation for the cropped image; and generating a full body EASI score based on the eczema segmentation and the intermediate scores for displaying on a device to assist in diagnosis.

Abstract: A method of operation of a compute system includes: generating a skin segmentation including a non-skin region and a skin prediction based on a patient image; generating a body part segmentation based on the patient image; generating a cropped image based on the skin segmentation and the body part segmentation with the cropped image includes the non-skin region based on the skin prediction; generating a lesion segmentation based on the cropped image and the skin prediction; generating intermediate scores for erythema, induration, desquamation for the cropped image; and generating a full body severity score based on the lesion segmentation and the intermediate scores for displaying on a device to assist in diagnosis.

Abstract: A method of operation of a compute system includes: generating a skin segmentation including a non-skin region and a skin prediction based on a patient image; generating a body part segmentation based on the patient image; generating a cropped image based on the skin segmentation and the body part segmentation with the cropped image includes the non-skin region based on the skin prediction; generating a psoriasis segmentation based on the cropped image and the skin prediction; generating intermediate scores for erythema, induration, desquamation for the cropped image; and generating a full body PASI score based a psoriasis segmentation and the intermediate scores on the for displaying on a device to assist in diagnosis.

Abstract: The present disclosure relates to a plant disease control composition comprising a culture broth of Streptomyces sp. JCK-6141 strain or an extract of the culture broth of the strain, a preparation method therefor and a plant disease control method. The culture broth of the strain or an extract thereof exhibits extremely excellent antimicrobial activity when applied to a causative pathogen of plant disease and thus may be effectively used for plant disease control.

Abstract: Systems and methods for downhole gravel packing operations. The systems include a pressure activated fluid diverter and a plurality of inflow control devices attached to the pressure activated fluid diverter to form a section of a string. The pressure activated fluid diverter has a flow performance curve defined by: DP=AQ3+BQ2+CQ, where DP is the differential pressure across the pressure activated fluid diverter in psi and Q is the flow rate through the pressure activated fluid diverter in bpm, with a fluid passing therethrough being at room temperature, having a fluid density of 9.2 ppg, and a fluid viscosity of 1 cps, the flow performance curve of the pressure activated fluid diverter is equal to or below a maximum flow performance curve defined by A=0, B=313.43, C=?1.715.

Abstract: Systems and methods for downhole gravel packing operations are described. The systems include a pressure activated fluid diverter and a plurality of inflow control devices attached to the pressure activated fluid diverter to form a section of a string. The pressure activated fluid diverter has a flow performance curve defined by: DP=AQ3+BQ2+CQ, where DP is the differential pressure across the pressure activated fluid diverter in psi and Q is the flow rate through the pressure activated fluid diverter in bpm, with a fluid passing therethrough being at room temperature, having a fluid density of 9.2 ppg, and a fluid viscosity of 1 cps, the flow performance curve of the pressure activated fluid diverter is equal to or below a maximum flow performance curve defined by A=0, B=313.43, C=?1.715.

Abstract: Provided are a scalable data fusion method and related products. The scalable data fusion method is applied in a central device and includes: receiving sensing data transmitted by each of M first edge devices, wherein M is an integer equal to or greater than 1; fusing the sensing data transmitted by each of the M first edge devices to obtain M pieces of fused data respectively corresponding to the M first edge devices; distributing the M pieces of fused data to the M first edge devices respectively; receiving object information transmitted by each of the M first edge devices, wherein the object information is obtained based on the fused data; and integrating the object information transmitted by each of the M first edge devices and construct surrounding information based on the integrated object information.

Abstract: The present disclosure provides a method for checking a to-be-checked signal and related products. The method is applied in a checking device and includes: a first obtaining module, configured to obtain a to-be-checked signal carrying first control information, wherein the first control information is generated based on original control information; a second obtaining module, configured to obtain original checking information; a determining module, configured to determine the first control information according to the to-be-checked signal; and a checking module, configured to check correctness of the first control information according to the original checking information. The present disclosure can be used to enable reliability and functional safety on devices originally designed without features intended to support those functions.

Abstract: The present disclosure provides a method for checking a to-be-checked signal and related products. The method is applied in a checking device and includes: a first obtaining module, configured to obtain a to-be-checked signal carrying first control information, wherein the first control information is generated based on original control information; a second obtaining module, configured to obtain original checking information; a determining module, configured to determine the first control information according to the to-be-checked signal; and a checking module, configured to check correctness of the first control information according to the original checking information. The present disclosure can be used to enable reliability and functional safety on devices originally designed without features intended to support those functions.

Abstract: Provided are an acceleration method, an apparatus and a system on chip. The acceleration method includes: the accelerator receives N-th parameter information of an N-th layer from a controller, wherein M layer of the deep neural network correspond to M application specific integrated circuit in the accelerator, wherein M and N are positive integer, M?2, 1?N?M, executes computation of the N-th layer according to the N-th parameter information, and transmits N-th computation result information of the N-th layer indicates that the computation of the N-th layer is completed, to the controller, wherein the computation result information comprises computation result of the N-th layer. Then a complete flexibility for ASIC implementations of hardware accelerator can be achieved, and any kind of DNN based algorithms can be supported, which improves the universality of the accelerator.