Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a long-standing challenge in ultrasound-assisted procedures. We aim to define new ways of parameterising long-term dependencies, and evaluate the performance. Methods: First, long-term dependency is encoded by transformation positions within a frame sequence. This is achieved by combining a sequence model with a multi-transformation prediction. Second, two dependency factors are proposed, anatomical image content and scanning protocol, for contributing towards accurate reconstruction. Each factor is quantified experimentally by reducing respective training variances. Results: 1) The added long-term dependency up to 400 frames at 20 frames per second (fps) indeed improved reconstruction, with an up to 82.4% lowered accumulated error, compared with the baseline performance. The improvement was found to be dependent on sequence length, transformation interval and scanning protocol and, unexpectedly, not on the use of recurrent networks with long-short term modules; 2) Decreasing either anatomical or protocol variance in training led to poorer reconstruction accuracy. Interestingly, greater performance was gained from representative protocol patterns, than from representative anatomical features. Conclusion: The proposed algorithm uses hyperparameter tuning to effectively utilise long-term dependency. The proposed dependency factors are of practical significance in collecting diverse training data, regulating scanning protocols and developing efficient networks. Significance: The proposed new methodology with publicly available volunteer data and code for parametersing the long-term dependency, experimentally shown to be valid sources of performance improvement, which could potentially lead to better model development and practical optimisation of the reconstruction application.

The evaluation on clustering results is an important component of clustering analysis, which can be conducted by the cluster validity index. However, the performances of most existing indices depend on not only the specific clustering algorithms but also the measurements of within-and between-cluster distances and data structures, resulting in limited applications in practice. In this paper, a new within-cluster distance under a general assumption is defined first. After adjusting within-cluster distances of each point according to the adjustment rule, a novel cluster validity index is proposed. Moreover, the notion of chain is introduced to eliminate the effects of sizes, densities, and shapes of clusters. This index does not need any prior information about clustering algorithms and is independent of data structures. Two groups of synthetic datasets with various characteristics and real-world datasets are used to validate this proposed validity index. Experimental results demonstrate that the evaluation accuracy of this index is higher than that of the existing typical indices and performs well on datasets with irregular-shaped clusters.

Electrical impedance tomography (EIT) is a radiation-free, fast, and convenient tomographic technology that uses only surface electrodes. EIT has the potential to provide continuous bedside monitoring of patients, for example, during mechanical ventilation. However, the soft-field nature of EIT leads to an ill-posed reconstruction problem that often results in multiple image solutions. This is exacerbated when EIT systems are used in variable measurement setups and scenarios, as few adjustment methods or EIT calibration approaches work reliably or require a reference phantom. The goals of this work are to design quality metrics to provide feedback for the EIT process to guide the system in adjusting the algorithm and parameters, for example, to alert the system in the event of poor image quality due to electrode connection variability. This then may be dealt with by physical electrode adjustment. In this article, the metrics, OAD and AL, representing object and artifact discreteness and artifact level, were designed to evaluate EIT image quality in an unsupervised way. Based on the quality index feedback, an image optimization process is proposed. The developed metrics and optimization methods were validated through data simulated in typical circular domain and thorax scenarios, and clinical data from human respiration. The conventional supervised evaluation metric, i.e., the correlation coefficient (COR) between the reference image and the reconstructed image, was used for comparison. The accuracy of new metrics was represented as the correlation coefficient of the new metric and COR. From the results, OAD and AL could improve the image quality by optimizing regularization parameters. The accuracy results of OAD and COR in the circular domain are 0.9429 and 0.9328, and those in thorax scenarios are 0.9741 and 0.8393. OAD and AL provide the evaluation of EIT images without using any prior knowledge or reference conductivity distribution, providing feedback to improve the reliability of EIT systems and reduce the risk of erroneous diagnosis.

Sharding is an important technology that utilizes group parallelism to enhance the scalability and performance of blockchain. However, the existing solutions use a historical transaction-based approach to reallocate shards, which cannot handle temporary overload and incurs additional overhead during the reallocation process. To this end, this paper proposes LMChain, an efficient load-migratable beacon-based sharding blockchain system. The primary goal of LMChain is to eliminate reliance on historical transactions and achieve the high performance. Specifically, we redesign the state maintenance data structure in Beacon Shard to effectively manage all account states at the shard level. Then, we innovatively propose a load-migratable transaction processing protocol built upon the new data structure. To mitigate read-write conflicts during the selection of migration transactions, we adopt a novel graph partitioning scheme. We also adopt a relay-based method to handle cross-shard transactions and resolve inter-shard state read-write conflicts. We implement the LMChain prototype and conduct experiments in a real network environment comprising 17 cloud servers. Experimental results show that, compared with state-of-the-art solutions, LMChain effectively reduces the average transaction waiting latency of overloaded transactions by 30% to 48% in different cases within 16 transaction shards, while improving throughput by 3% to 10%.

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.