Hyperledger Fabric is one of the most popular federation chains and is widely used in many fields, such as healthcare, government, and education. However, in practical application scenarios, Fabric faces the challenge of handling a large number of aborted transactions. These transaction failures are primarily caused by read-write conflicts resulting from read-write locks during the execution and validation phases, as well as transaction ordering dependencies in the ordering phase. The frequent occurrence of aborted transactions significantly reduces system throughput, as each failed transaction not only wastes computational resources and network bandwidth but also requires reprocessing. Therefore, this paper proposes a solution called Fabric*, which includes a transaction reordering mechanism, a cache queue mechanism, and a lock-free mechanism. The former two mechanisms are used to abort conflicting transactions early in the ordering phase and to avoid aborting conflicting transactions again. The latter mechanism is used to abort transactions that read obsolete data during the simulation phase. Fabric* reduces abort transactions and increases the throughput of successful transactions in the system. Experimental results show that Fabric* improves throughput by up to about 23.9% and 7.4% over the original Fabric and Fabric++.

The efficiency of data retrieval is crucial for the lifecycle traceability of large-scale manufacturing products. This article proposes a novel on-chain and off-chain parallel retrieval method based on blockchain and Merkle Patricia Trie (MPT), which is called parallel retrieve index MPT (PRIMPT). In the proposed method, index MPT (InMPT) is designed to store the index information and the off-chain address of transactions based on MPT. In addition, the transaction division and the node division are proposed to realize the parallelization. Among them, the transaction division adds a new stage branch node under the root node of InMPT and divides transactions according to their stages, while the node division groups the nodes in the blockchain according to the derived most efficient group number and assigns retrieval requests to these node groups. Experiment results show that the PRIMPT method improves the efficiency by 44% compared with the state-of-the-art on-chain and off-chain method.

Abstract Semantic segmentation is a challenging task in computer vision which is widely used in autonomous driving and scene understanding. State‐of‐the‐art semantic segmentation networks, like DeepLab and PSPNet, make full use of multiple feature information to improve spatial resolution. However, the feature resolution in the scale‐axis is not dense enough for practical applications. To tackle this problem, a multi‐stream network is designed with atrous convolutional layers at multiple rates to capture objects and context at multiple scales. Furthermore, intra‐connections and inter‐connections are designed to fuse multi‐scale features densely which produce a feature pyramid with much larger scale diversity and larger receptive field by involving small quantity of computation. The proposed module can be easily used in other methods and it helps to increase the performance. Compared with existing methods, the proposed network, called Multi‐stream Densely Connected Network, reaches competitive results on ADE20K dataset, PASCAL VOC 2012 dataset, and Cityscapes dataset.

In order to solve the problem the homogeneous coefficient of the classic active demons algorithm can not take into account large deformation and small deformation at the same time, this paper presents a non-rigid registration algorithm based on active demons algorithm. The proposed algorithm introduces a new parameter called balance coefficient to the active demons algorithm, which will adjust the driving force combined with homogeneous coefficient. Not only the large deformation and the small deformation can be taken into account at the same time, but also the mutual restraint problem of the convergence speed and the registration accuracy can be eased in a certain extent. In order to further improve the registration accuracy and the convergence speed, and avoid falling into local extreme value, a coarse-to-fine multi-resolution strategy is introduced into the registration process. Experiments on checkboard test images, natural images and medical images demonstrate that the proposed method is faster and more accurate, and the registration accuracy is close to the latest TV-L1 optical flow image registration algorithm, which solves the problems of the active demons algorithm.