Abstract: Disclosed are an OTA test method for a vehicle antenna system, a device and a storage medium. According to the method, the radio frequency performance of a physical layer of a whole vehicle antenna system can be tested, and the problem in the prior art that the test result of parts cannot accurately reflect the performance of the whole vehicle antenna system can be solved.

Abstract: In one or more embodiments, the present invention is directed to a blockchain secured, software-defined network and monitoring system comprising: a multi-controller software-defined network (SDN) network layer; a blockchain based security and autonomy layer; a deep learning-driven decision making layer comprising the one or more computational centers and a horizontal data plane layer. In some embodiments, the present invention is directed to methods for ensuring the integrity of a control commands and optimizing performance and security using the blockchain secured, software-defined network and monitoring system. In various embodiments, the present invention relates to methods for extracting useful features from said labelled and non-labelled data contained in the horizontal data plane layer in the blockchain secured, software-defined network and monitoring system using a knowledge domain-enabled hybrid semi-supervision learning method.

Abstract: A method of controlling a mobile work machine includes receiving an image of spectral response at an area of terrain corresponding to a path of the mobile work machine, generating an image distance metric based on a distance between the spectral response and a base spectral response model corresponding to the terrain, comparing the image distance metric to a distance threshold, and controlling a controllable subsystem of the mobile work machine based on the comparison.

Abstract: A method of controlling a mobile work machine includes receiving an image of spectral response at an area of terrain corresponding to a path of the mobile work machine, generating an image distance metric based on a distance between the spectral response and a base spectral response model corresponding to the terrain, comparing the image distance metric to a distance threshold, and controlling a controllable subsystem of the mobile work machine based on the comparison.

Abstract: In one or more embodiments, the present invention is directed to a blockchain secured, software-defined network and monitoring system comprising: a multi-controller software-defined network (SDN) network layer; a blockchain based security and autonomy layer; a deep learning-driven decision making layer comprising the one or more computational centers and a horizontal data plane layer. In some embodiments, the present invention is directed to methods for ensuring the integrity of a control commands and optimizing performance and security using the blockchain secured, software-defined network and monitoring system. In various embodiments, the present invention relates to methods for extracting useful features from said labelled and non-labelled data contained in the horizontal data plane layer in the blockchain secured, software-defined network and monitoring system using a knowledge domain-enabled hybrid semi-supervision learning method.

Abstract: A method of controlling a mobile work machine includes receiving an image of spectral response at an area of terrain corresponding to a path of the mobile work machine, generating an image distance metric based on a distance between the spectral response and a base spectral response model corresponding to the terrain, comparing the image distance metric to a distance threshold, and controlling a controllable subsystem of the mobile work machine based on the comparison.

Abstract: In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.

Abstract: A method of controlling a mobile work machine includes receiving an image of spectral response at an area of terrain corresponding to a path of the mobile work machine, generating an image distance metric based on a distance between the spectral response and a base spectral response model corresponding to the terrain, comparing the image distance metric to a distance threshold, and controlling a controllable subsystem of the mobile work machine based on the comparison.

Abstract: In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.

Abstract: A system for performing predictive analysis and diagnostics is disclosed. The system includes a plurality of sensors communicatively coupled to a vehicle electronics unit. The plurality of sensors are configured to generate at least one first signal indicative of a first sensed condition and at least one second signal indicative of a second sensed condition. A remote central processing system is coupled to the vehicle electronics unit. The remote central processing system comprises a remote processor and a remote data storage device, wherein the remote central processing system is configured to receive each of the at least one first and second signals.