Systems and methods for vacuum extraction for material sorting applications are provided. In one embodiments, a vacuum object sorting system comprises: a vacuum extraction assembly that includes at least one vacuum extractor device having an inlet and an outlet, wherein the at least one vacuum extractor device is configured to convert a controlled compressed air stream into a channeled vacuum airflow entering the inlet and exiting the outlet; and an object recognition device coupled to sorting control logic and electronics, wherein the controlled compressed air stream is controlled in response to a signal generated by the object recognition device; wherein the at least one vacuum extractor device is configured to capture a target object identified by the sorting control logic and electronics utilizing the channeled vacuum airflow, and further utilizing the channeled vacuum airflow, to pass the target object through the inlet and outlet to a deposit location.

Systems and methods for vacuum extraction for material sorting applications are provided. In one embodiments, a vacuum object sorting system comprises: a vacuum extraction assembly that includes at least one vacuum extractor device having an inlet and an outlet, wherein the at least one vacuum extractor device is configured to convert a controlled compressed air stream into a channeled vacuum airflow entering the inlet and exiting the outlet; and an object recognition device coupled to sorting control logic and electronics, wherein the controlled compressed air stream is controlled in response to a signal generated by the object recognition device; wherein the at least one vacuum extractor device is configured to capture a target object identified by the sorting control logic and electronics utilizing the channeled vacuum airflow, and further utilizing the channeled vacuum airflow, to pass the target object through the inlet and outlet to a deposit location.

The present disclosure provides a signal processing device, including a signal collector, an instruction converter, and a processor. Examples of the present disclosure may achieve precise recognition of users' intentions and bring operational conveniences to users.

The present disclosure provides a signal processing device, including a signal collector, an instruction converter, and a processor. Examples of the present disclosure may achieve precise recognition of users' intentions and bring operational conveniences to users.

Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

A processing system performs using an edge device and a server device, wherein the edge device includes processing circuitry configured to process processing target data and output a processing result of the processing target data, determine that the server device is to execute processing related to the processing target data when an evaluation value for evaluating which of the edge device and the server device is to process the processing target data satisfies a condition, determine that the evaluation value is included in a range for determining that processing is to be executed by the edge device when the processing result of the processing target data satisfies a predetermined evaluation, and output the processing result of the processing target data processed, and transmit data that causes the server device to execute the processing related to the processing target data when determining that the server device is to execute the processing.

To allow arithmetic processing using a plurality of processing nodes to be executed with a smaller memory size, an arithmetic processing apparatus for executing processing using a hierarchical type network formed by the plurality of processing nodes, comprises: a storage unit configured to store a parameter used by each of the plurality of processing nodes for arithmetic processing and a calculation result of the arithmetic processing in each of the plurality of processing nodes; and a buffer control unit configured to switch, based on a configuration of the hierarchical type network, a buffer system of the parameter and the calculation result in the storage unit in at least one layer of the hierarchical type network.