At the end of 2019, the COVID-19 outbreak emerged abruptly. Chinese health authorities highlighted the role of CT scans, X-rays, and other computerized lung imaging in aiding COVID-19 diagnosis. This study aims to develop a computer-based system to assist healthcare professionals in diagnosing COVID-19 infections based on computerized imaging analysis. This approach aims to alleviate the workload of COVID-19 specialists, improving diagnostic and treatment efficiency and allowing specialists to focus on devising appropriate patient care plans promptly. The proposed method focuses on analyzing COVID-19 lesion characteristics within individual CT slices and their serial characteristics across CT sequences. This approach mirrors the diagnostic process of radiologists closely. To validate our model, we compiled a dataset from real medical diagnostic settings, minimizing the impact of lesion-like artifacts. We conducted a series of comparative and ablation experiments to evaluate the model's performance. Results indicate that our model outperforms the classic classification models and other commonly used models for COVID-19 diagnosis on our constructed dataset.

The privacy-preserving localization of hydroacoustic sensor networks plays a critical role in the communication and control of marine environments. The performance of underwater location varies with constrained the complex underwater environment, such as openness, inhomogeneity, temperature, press, and so on, which make it much more challenging to ensure privacy preserving methods and obtain accurate acoustic speed used for localization computation. To address the above issues, this paper innovatively constructs Privacy-preservation Three-dimensional Underwater Location (PTUL). Firstly, the maximum distance separable coding algorithm which is designed one-time aggregated mask reconstruction by mask coding of online beacon node signals to ensure privacy-preserving and robustness is introduced into this localization model. Secondly, it relies on the sound speed modified model to compensate for the error of acoustic speed, through which an iterative regression strategy is used to deal with the change of acoustic speed. Finally, the experiments are provided to illustrate the feasibility of the proposed model. The proposed localization algorithm can efficiently improve the localization accuracy and ensure the privacy of the localization data compared with the other localization algorithms.

Localization is essential for Underwater Sensor Networks (UWSN), as the information obtained by UWSNs must have accurate positions. Most of the existing location algorithms rely on the range, but the particularity of underwater acoustic communication seriously affects the ranging accuracy, which greatly reduces the availability of range-based localization algorithms. The malicious node interference also affects the positioning accuracy and causes harm to UWSN. To solve these problems, a Range-free and Level-based Localization scheme (RLL) is proposed in this paper, which establishes a tree structure for the nodes by moving beacons. Based on the tree structure, it divides these nodes into levels and zones. Signal strength is not used for ranging, but to calculate the relative position relationship between nodes and narrow the zones, localization is achieved by cooperation of nodes between adjacent zones. In addition, an effective protection mechanism to identify malicious nodes by level and zonal relationships is also proposed. RLL could be used in both static and dynamic situations. The simulations show that the localization effect of RLL is much better than the existing algorithms, and it can effectively detect malicious nodes to protect network security.