Abstract: The present invention relates to the technical field of bone processing, and in particular to a composite device for autologous bone harvesting and bone processing, including: a bone harvesting auxiliary device and a bone processing device. The bone harvesting auxiliary device includes: a bone harvesting caliper, a guide sleeve, and a depth-limiting Kirschner wire. The bone processing device includes: a bone box, a fastening assembly, and a cutting assembly. The composite device for autologous bone harvesting and bone processing in the present invention is capable of precise harvesting based on preoperative imaging measurements, thereby preventing resource waste. Intraoperatively, the device can obtain cancellous bone, unicortical bone, bicortical bone, and tricortical bone. In addition, various finished bone products of different shapes and sizes can be prepared according to requirements for a bone grafting area, such as bone granules, bone strips, bone chips, and bone blocks.

Abstract: Disclosed in the present invention is a method for decoding Chinese character writing for an invasive brain-computer interface. In a practical application, a corresponding motor neural signal is divided into two states of a writing stroke and a writing stroke break in view of inconsistency of the writing stroke and the writing stroke break during Chinese character writing, and different filters are trained. A hidden markov model (HMM algorithm) and a Viterbi algorithm are used to judge a task state of the motor neural signal, and the corresponding signal is put into a corresponding decoder. The present invention effectively reduces influence of difference of neural data in different states on the decoder, and improves the performance and robustness of the decoder.

Abstract: Disclosed in the present invention is a method for decoding Chinese character writing for an invasive brain-computer interface. In a practical application, a corresponding motor neural signal is divided into two states of a writing stroke and a writing stroke break in view of inconsistency of the writing stroke and the writing stroke break during Chinese character writing, and different filters are trained. A hidden markov model (HMM algorithm) and a Viterbi algorithm are used to judge a task state of the motor neural signal, and the corresponding signal is put into a corresponding decoder. The present invention effectively reduces influence of difference of neural data in different states on the decoder, and improves the performance and robustness of the decoder.

Abstract: The disclosed computer-implemented method may include accessing various antenna elements and identifying parameters for an antenna that is to be formed using the accessed antenna elements. The method may also include assembling the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include generating, using a machine-learning model of a computing device, a set of antenna designs. The method may also include tokenizing, by the computing device, each antenna design in the generated set of antenna designs. Additionally, the method may include predicting, by the machine-learning model of the computing device, a frequency response for each tokenized antenna design. Furthermore, the method may include comparing, by the computing device, the frequency response for each tokenized antenna design. Finally, the method may include selecting, by the computing device based on the comparison, an antenna design that meets a performance threshold for the frequency response. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include accessing various antenna elements and identifying parameters for an antenna that is to be formed using the accessed antenna elements. The method may also include assembling the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters. Various other methods, systems, and computer-readable media are also disclosed.