Anonymous credentials are ideal for decentralized systems like blockchains, as they enhance privacy, security, and regulatory compliance while maintaining flexibility and adaptability. These decentralized systems often require features such as weighted threshold issuance and tracing for elasticity of decision-making, fine-grained blindness for user privacy control, and issuer-hiding to reduce potential external threats. However, the latest advancements in anonymous credential schemes cannot meet all of these essential properties for blockchain systems. To address these challenges, we propose weighted threshold anonymous credentials with redactable fine-grained blind signature (WTAC). Firstly, by leveraging unlinkable redactable signatures (URS) and functional encryption techniques, our redactable fine-grained blind signature supports selective disclosure and permits credential issuers to learn a particular function value related to the attributes during issuance without revealing the actual attribute content. Secondly, we utilize weighted ramp secret-sharing (WRSS) and randomizable signatures to achieve weighted threshold anonymous credentials, which are issuer-hiding to both users and verifiers. Moreover, we provide a concrete construction instantiated by the Fiat-Shamir paradigm and demonstrate its application, a privacy-preserving auditable lending system in blockchain scenarios, achieving the integration of user privacy and regulatory compliance. Finally, we give a formal security proof of anonymity, fine-grained blindness, traceability, non-frameability, and issuer-hiding. Performance evaluation shows the practicability and efficiency of our proposed WTAC.

Programmable manipulation of fluid-based soft robots has recently attracted considerable attention. Achieving parallel control of large-scale ferrofluid droplet robots (FDRs) is still one of the major challenges that remain unsolved. In this article, we develop a distributed magnetic field control platform to generate a series of localized magnetic fields that enable the simultaneous control of many FDRs, allowing teams of FDRs to collaborate in parallel for multifunctional manipulation tasks. Based on the mathematical model using the finite element method, we first evaluate the distribution properties of the local magnetic fields as well as the gradients generated by individual electromagnets. Meanwhile, the locomotion and deformation behavior of the FDR is also characterized to verify the actuation performance of the developed system. Subsequently, a vision-based closed-loop feedback control strategy is then presented, which aims to achieve path tracking of multiple robot formations. Thermal analysis shows that the system's low output power enables reliable and sustained long-term operation. Finally, the developed system is tested through extensive physical experiments with different numbers of FDRs. The results demonstrate the potential of the designed setup in manipulating dozens of FDRs for digital display, message encoding, and microfluidic logistics. To the best of our knowledge, this is the first attempt that allows independent control of such scale droplet robots (up to 72) for cooperative applications.

Central Pattern Generators (CPG) nonlinear oscillation network is being increasingly used in the control of multi-joint collaborative robots. The motion attitude of robots can be effectively adjusted by tuning parameters of the CPG neural network. However, the mapping from CPG parameters to motion attitude is relatively complicated. To improve the motion performance, an optimization method combining computational fluid dynamics (CFD) and CPG network is proposed. In this work, we design a three-joint biomimetic soft robot fish following the body structure of trevally and an improved CPG network based on the Hopf model is incorporated into the control system. Directly optimizing the swimming performance through experiments is time consuming and complex, a mode of first adjusting parameters on the simulation platform and then refining on the robot is usually adopted. Therefore, a CFD simulation platform using hydrodynamic solutions has been established to assist in analyzing the swimming effect. Finally, the experimental results show that the swimming simulation by the CFD is highly similar to the real test, and the swimming performance after the improved CPG network optimization has been significantly increased.