OBJECTIVE: We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS: We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS: The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION: The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION: Blockchain technology can be used to support federated data analysis among multiple institutions.

Secure Remote Password (SRP) protocol is an essential password-authenticated key exchange (PAKE) protocol based on the discrete logarithm problem (DLP). The protocol is specifically designed to obtain a session key and it has been widely used in various scenarios due to its attractive security features. In the SRP protocol, the server is not required to save any data directly associated with passwords. And this makes attackers who manage to corrupt the server fail to impersonate the client unless performing a brute-force search for the password. However, the development of quantum computing has potentially made classic DLP-based public-key cryptography schemes not secure, including the SRP protocol. So it is significant to design a quantum-resistant SRP protocol. In this paper, based on the original scheme, we propose a post-quantum SRP protocol from the learning with errors (LWE) problem. And we give rigorous proof and analyses on the correctness and security of the scheme. Besides being resistant to known quantum attacks, it maintains the various secure qualities of the original protocol.

According to the requirements of informational weapon equipment, the application of polymorphic real-time computation technology in complex embedded system is studied in this paper. The polymorphic real-time computation model for unmanned aerial vehicle(UAV) is designed and verified from the aspects of modal management and polymorphic tasks. The simulation results show that the proposed model can provide modal decision and ensure its real-time calculation to adapt the demand change of UAV system.

This paper proposes a new three-switch single-phase Z-source inverter (ZSI) based on SEPIC converters, which can be named SEPIC-based ZSI. This topology has the characteristics of buck-boost capability and has the feature of dual grounding. In addition, the voltage gain of proposed inverter is higher than that of the single-phase quasi-Z-source and semi-Z-source inverters. At the same time, conventional control methods can be used due to the linear voltage gain. The operation of current topology is described. Finally, the performance of current topology is verified through the simulation.

This paper proposes a new three-switch single-phase Z-source inverter (ZSI) based on CUK converter, which is named CUK-based ZSI. This topology has the characteristics of buck-boost capability and dual grounding. In addition, the voltage gain of proposed inverter is higher than those of the single-phase quasi-Z-source and semi-Z-source inverters. Aside from that, a simple control method is presented to achieve the linear voltage gain. The operation principle of proposed topology is described. Finally, the performance evaluation is carried out and the test results verify the effectiveness of the proposed solution.

This paper investigates two surface electromyogram (sEMG)-based control strategies developed for a power-assist exoskeleton arm. Different from most of the existing position control approaches, this paper develops force control methods to make the exoskeleton robot behave like humans in order to provide better assistance. The exoskeleton robot is directly attached to a user's body and activated by the sEMG signals of the user's muscles, which reflect the user's motion intention. In the first proposed control method, the forces of agonist and antagonist muscles pair are estimated, and their difference is used to produce the torque of the corresponding joints. In the second method, linear discriminant analysis-based classifiers are introduced as the indicator of the motion type of the joints. Then, the classifier's outputs together with the estimated force of corresponding active muscle determine the torque control signals. Different from the conventional approaches, one classifier is assigned to each joint, which decreases the training time and largely simplifies the recognition process. Finally, the extensive experiments are conducted to illustrate the effectiveness of the proposed approaches.

The high conventional accuracy of pattern recognition-based surface myoelectric classification in laboratory experiments does not necessarily result in high accessibility to practical protheses. An obvious reason is the effect of signals of untrained classes caused by the relatively small training dataset. In order to make the classifier robust to untrained classes, a classification scheme is developed based on boosting and random forest classifiers in this paper. Meanwhile, a threshold, the post probability of the prediction, is introduced as a balance (i.e., adjust) between the accurate classification and the rejection of the samples belonging to some untrained classes. The experiments are conducted to compare with other two schemes using linear discriminant analysis and support vector machines. Surface electromyogram signals, labeled with seven isometric movements, are collected from six healthy subjects' forearm. It is shown that the proposed scheme can reach up to about 92% accuracy in recognizing trained classes and 20% for untrained classes. Through adjusting the threshold, the accuracy of rejecting untrained classes reaches up to around 80%, with small decrease in recognizing trained classes (down to 80%). In the analysis of experiments' results, we also find that the proposed scheme has better error distribution among the classes.

Cyclic codes with two zeros and their dual codes as a practically and theoretically interesting class of linear codes have been studied for many years and find many applications. The determination of the weight distributions of such codes is an open problem. Generally, the weight distributions of cyclic codes are difficult to determine. Utilizing a class of elliptic curves, this paper determines the weight distributions of dual codes ofq-ary cyclic codes with two zeros for a few more cases, whereqis an odd prime power.