Range query is the hot topic of the privacy-preserving data publishing. To preserve privacy, the large range query means more accumulate noise will be injected into the input data. This study presents a research on differential privacy for range query via Haar wavelet transform and Gaussian mechanism. First, the noise injected into the input data via Laplace mechanism is analyzed, and we conclude that it is difficult to judge the level of privacy protection based on the Haar wavelet transform and Laplace mechanism for range query because the sum of independent random Laplace variables is not a variable of a Laplace distribution. Second, the method of injecting noise into Haar wavelet coefficients via Gaussian mechanism is proposed in this study. Finally, the maximum variance for any range query under the framework of Haar wavelet transform and Gaussian mechanism is given. The analysis shows that using Haar wavelet transform and Gaussian mechanism, we can preserve the differential privacy for each input data and any range query, and the variance of noise is far less than that just using the Gaussian mechanism. In an experimental study on the dataset age extracted from IPUM’s census data of the United States, we confirm that the proposed mechanism has much smaller maximum variance of noises than the Gaussian mechanism for range-count queries.

The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs the K-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.

With the advent of artificial intelligence, ship segmentation has become a critical component in the development of intelligent maritime surveillance systems. However, due to the increasing number of ships and the increasingly complex maritime traffic environment, the target features in these ship images are often not clear enough, and the key details cannot be clearly identified, which brings difficulty to the segmentation task. To tackle these issues, we present an approach that leverages state-of-the-art technology to improve the precision of ship segmentation in complex environments. Firstly, we employ a multi-scale context features module using different convolutional kernels to extract a richer set of semantic features from the images. Secondly, an enhanced spatial pyramid pooling (SPP) module is integrated into the encoder’s final layer, which significantly expands the receptive field and captures a wider range of contextual information. Furthermore, we introduce an attention module with a multi-scale structure to effectively obtain the interactions between the encoding–decoding processes and enhance the network’s ability to exchange information between layers. Finally, we performed comprehensive experiments on the public SeaShipsSeg and MariBoatsSubclass open-source datasets to validate the efficacy of our approach. Through ablation studies, we demonstrated the effectiveness of each individual component and confirmed its contribution to the overall system performance. In addition, comparative experiments with current state-of-the-art algorithms showed that our MSCF-Net excelled in both accuracy and robustness. This research provides an innovative insight that establishes a strong foundation for further advancements in the accuracy and performance of ship segmentation techniques.

The segmentation of visible ship images is an important part of intelligent ship monitoring systems. However, this task is faced with many difficulties in practical applications, such as complex background environments, variations in illumination, and target scale changes. In view of these situations, we present a dual-branch decoder network with a multiscale cascaded residual module for ship segmentation. Specifically, in the encoding stage, we introduce a multiscale cascaded residual module as a replacement for traditional convolution layers. By leveraging its multiscale architecture, the module effectively captures both the global context and fine-grained details. In the decoding phase, our framework incorporates two parallel branches, both of which utilize the cascading residual module to enhance feature extraction and representation. Additionally, one of the branches is equipped with spatial attention and channel attention mechanisms. Finally, comprehensive tests were conducted on the publicly available ship datasets MariBoatsSubclass and SeaShipsSeg. Our proposed network achieved impressive results, with Dice, Recall, Mcc, and Jaccard scores of 0.9003, 0.9105, 0.8706, and 0.8197 on the MariBoatsSubclass dataset. Similarly, it demonstrated outstanding performance on the SeaShipsSeg dataset, attaining Dice, Recall, Mcc, and Jaccard scores of 0.9538, 0.9501, 0.9519, and 0.9129, respectively. These results highlight the superior accuracy and robustness of DBD-Net in segmenting and detecting ships across diverse scenarios and datasets.