Existing Two-Dimensional (2D) rectal tumor segmentation methods struggle to fully exploit the relationships between slices due to the absence of Three-Dimensional (3D) spatial information, while traditional 3D segmentation techniques often suffer from poor performance and low rotational robustness. To address these limitations, we propose a 3D Magnetic Resonance (MR) segmentation model based on a deep supervised residual capsule network (namely DRCU-Net). This model introduces a capsule module built on the 3D U-Net architecture, allowing it to capture the spatial hierarchical features of tumor tissue through a dynamic routing mechanism, thereby enhancing the model’s robustness to rotation. Additionally, we employ deep supervision mechanisms to improve model performance and modify the LayerNorm function to extend layer normalization to 3D, facilitating its adaptation to the processing requirements of 3D data. We evaluate our model on the dataset from Shanxi Cancer Hospital (China), it achieves a Dice score of 0.7580 and a Mean Intersection over Union (MIoU) score of 0.6258, which demonstrating its superiority.

Using global navigation satellite system (GNSS) to monitor snow depth helps scientists study the impacts of climate change and predict future climate patterns. In the process of extracting reflection signals from signal-to-noise ratio (SNR) data, traditional methods usually use low order polynomials for detrending terms. However, this traditional algorithm cannot completely eliminate the environmental noise in the SNR during signal decomposition, resulting in other noise sources still existing in the detrended SNR, which affects the inversion results. In order to make the inversion results more accurate, this paper proposes a robust empirical mode decomposition (REMD) based method. In signal decomposition, REMD is applied to improve the algorithm, and the correlation coefficient method is used to denoise the decomposed signal. The proposed algorithm is validated using the data from the U.S. Plate Boundary Observatory network SG27 site from winter 2016 to spring 2017 as the study data. The obtained experimental results are compared with the actual snow depth provided by Snowpack Telemetry. When the improved algorithm is used, the root mean square error and mean absolute error of the snow depth inversion at the SG27 site are improved, respectively.

As individuals age, their facial features change, which can hinder the accuracy of face recognition technology. To address this challenge, a new cross-age face recognition algorithm, leveraging deep learning and a loss function (Loss), has been proposed in this article. The Retinaface algorithm detects faces in images, while the Resnet-50 model is enhanced by incorporating an attention mechanism and improved softmax loss (Arcface) to extract facial features. This approach has been tested on publicly available and custom-built datasets, and its performance has been compared to other cross-age face recognition techniques. The results show that the model effectively recognizes faces across different age groups.

Working as aerial base stations, mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area. Herein, a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users, while considering the mobility of on-ground devices. In this paper, to solve this issue, we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range. Then, we propose a hybrid deployment algorithm based on the improved quick artificial bee colony. The algorithm is composed of a centralized deployment algorithm and a distributed one. The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed. Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

Severe cardiovascular diseases can rapidly lead to death. At present, most studies in the deep learning field using electrocardiogram (ECG) are performed on intra-patient experiments for the classification of coronary artery disease (CAD), myocardial infarction, and congestive heart failure (CHF). By contrast, actual conditions are inter-patient experiments. In this study, we proposed a deep learning network, namely, CResFormer, with dual feature extraction to improve accuracy in classifying such diseases. First, fixed segmentation of dual-lead ECG signals without preprocessing was used as input data. Second, one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature extraction. Then, ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract features. Finally, the Softmax function was used for classifications. Notably, the focal loss function is used when dealing with unbalanced datasets. The average accuracy, sensitivity, positive predictive value, and specificity of four classifications of severe cardiovascular diseases are 99.84%, 99.68%, 99.71%, and 99.90% in intra-patient experiments, respectively, and 97.48%, 93.54%, 96.30%, and 97.89% in inter-patient experiments, respectively. In addition, the model performs well in unbalanced datasets and shows good noise robustness. Therefore, the model has great application potential in diagnosing CAD, MI, and CHF in the actual clinical environment.

Although deep learning methods have recently attracted considerable attention in the medical field, analyzing large-scale electronic health record data is still a difficult task. In particular, the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions. This study uses data from the Medical Information Mart for Intensive Care database. Compared with structured data, unstructured data contain abundant patient information. However, this type of data has unsatisfactory characteristics, e.g., many colloquial vocabularies and sparse content. To solve these problems, we propose the KTI-RNN model for unstructured data recognition. The proposed model overcomes sparse content and obtains good classification results. The term frequency-inverse word frequency (TF-IWF) model is used to extract the keyword set. The latent dirichlet allocation (LDA) model is adopted to extract the topic word set. These models enable the expansion of the medical record text content. Finally, we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network (BiRNN) model and the output layer. We call it gated-attention-BiRNN (GA-BiRNN) and use it to identify heart failure from extensive medical texts. Results show that the $F\mathsf{}\mathrm{1}$ score of the proposed KTI-RNN model is 85.57%, and the accuracy rate of the proposed KTI-RNN model is 85.59%.

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.

Computational Radio Frequency IDentification (CRFID) is a device that integrates passive sensing and computing applications, which is powered by electromagnetic waves and read by the off-the-shelf Ultra High Frequency Radio Frequency IDentification (UHF RFID) readers. Traditional RFID only identifies the ID of the tag, and CRFID is different from traditional RFID. CRFID needs to transmit a large amount of sensing and computing data in the mobile sensing scene. However, the current Electronic Product Code, Class-1 Generation-2 (EPC C1G2) protocol mainly aims at the transmission of multi-tag and minor data. When a large amount of data need to be fed back, a more reliable communication mechanism must be used to ensure the efficiency of data exchange. The main strategy of this paper is to adjust the data frame length of the CRFID response dynamically to improve the efficiency and reliability of CRFID backscattering communication according to energy acquisition and channel complexity. This is done by constructing a dynamic data frame length model and optimizing the command set of the interface protocol. Then, according to the actual situation of the uplink, a dynamic data validation method is designed, which reduces the data transmission delay and the probability of retransmitting, and improves the throughput. The simulation results show that the proposed scheme is superior to the existing methods. Under different energy harvesting and channel conditions, the dynamic data frame length and verification method can approach the theoretical optimum.

In wireless communication, the space-time anti-jamming method is widely applied because it shows better performance than the pure airspace and pure temporal anti-jamming methods. However, its application is limited by its computational complexity, and it cannot suppress narrowband interference that is in the same direction as the navigation signal. To solve these problems, we propose improved frequency filter to filter the narrowband interference from the desired signal direction in advance, meanwhile, an improved variable step Least Mean Square (LMS) method is proposed to complete the space-time array weights with fast iteration, thereby reducing computational complexity. The simulation results show that, compared with conventional methods, the anti-jamming capability of the proposed algorithm is significantly enhanced; and its complexity is significantly reduced.