Abstract: A zero calibration method includes: adding, based on a first forward kinematics model of a robotic arm of a robot, zero deviations of each joint to form a second forward kinematics model, and simultaneously solving the second forward kinematics model and the first forward kinematics model to obtain an end deviation function; sending target end pose instructions to the robotic arm in a current zero point situation, and acquiring a spatial deviation of an actual position of an end; inputting position information of each joint angle and the spatial deviation, after each stop of motion of the end, into the end deviation function, and performing phase shifting to obtain an objective optimization function; and solving the objective optimization function to obtain a zero deviation value.

Abstract: Embodiments of this specification provide computer-implemented methods, apparatuses, computer-readable media, and systems for secure multi-party computation. In an example secure multi-party computation method, a first party encrypts a first plaintext segment of target data by using a homomorphic encryption algorithm based on a public key held by the first party in a first key pair to obtain a first ciphertext segment. A second plaintext segment of the target data is owned by a second party. The first party sends the first ciphertext segment to the second party. The second party performs a homomorphic addition operation in the homomorphic encryption algorithm on the first ciphertext segment and the second plaintext segment of the target data to obtain ciphertext data of the target data. The ciphertext data is decrypted based on a private key in the first key pair.

Abstract: A zero calibration method includes: adding, based on a first forward kinematics model of a robotic arm of a robot, zero deviations of each joint to form a second forward kinematics model, and simultaneously solving the second forward kinematics model and the first forward kinematics model to obtain an end deviation function; sending target end pose instructions to the robotic arm in a current zero point situation, and acquiring a spatial deviation of an actual position of an end; inputting position information of each joint angle and the spatial deviation, after each stop of motion of the end, into the end deviation function, and performing phase shifting to obtain an objective optimization function; and solving the objective optimization function to obtain a zero deviation value.

Abstract: A battery cover includes a top cover assembly and an insulating support. The top cover assembly can be fixedly connected with a case to form an accommodation space for accommodating a core, and the top cover assembly can be welded with a tab on the core. The insulating support can be accommodated in the accommodation space. The insulating support and the top cover assembly form an accommodation cavity for accommodating the tab, and the insulating support is provided with a notch for the tab to pass through.

Abstract: A battery cover includes a top cover assembly and an insulating support. The top cover assembly can be fixedly connected with a case to form an accommodation space for accommodating a core, and the top cover assembly can be welded with a tab on the core. The insulating support can be accommodated in the accommodation space. The insulating support and the top cover assembly form an accommodation cavity for accommodating the tab, and the insulating support is provided with a notch for the tab to pass through.

Abstract: Embodiments of this specification provide computer-implemented methods, apparatuses, computer-readable media, and systems for secure multi-party computation. In an example secure multi-party computation method, a first party encrypts a first plaintext segment of target data by using a homomorphic encryption algorithm based on a public key held by the first party in a first key pair to obtain a first ciphertext segment. A second plaintext segment of the target data is owned by a second party. The first party sends the first ciphertext segment to the second party. The second party performs a homomorphic addition operation in the homomorphic encryption algorithm on the first ciphertext segment and the second plaintext segment of the target data to obtain ciphertext data of the target data. The ciphertext data is decrypted based on a private key in the first key pair.

Abstract: Various examples of methods and systems are provided for compressive imaging using approximate message passing with denoising. According to an aspect, a method includes applying an approximate message passing (AMP) conversion framework to a plurality of substantially linear measurements for conversion into a plurality of scalar measurements. A denoiser algorithm can be applied to the plurality of scalar measurements to generate a plurality of denoised scalar measurements. Further, a conversion term can be applied to the plurality of denoised scalar measurements for converting the plurality of denoised scalar measurements to a plurality of denoised substantially linear measurements. The plurality of substantially linear measurements can represent two-dimensional or three-dimensional signals.

Abstract: A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.

Abstract: A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.

Abstract: Various examples of methods and systems are provided for compressive imaging using approximate message passing with denoising. According to an aspect, a method includes applying an approximate message passing (AMP) conversion framework to a plurality of substantially linear measurements for conversion into a plurality of scalar measurements. A denoiser algorithm can be applied to the plurality of scalar measurements to generate a plurality of denoised scalar measurements. Further, a conversion term can be applied to the plurality of denoised scalar measurements for converting the plurality of denoised scalar measurements to a plurality of denoised substantially linear measurements. The plurality of substantially linear measurements can represent two-dimensional or three-dimensional signals.