With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

Multi-dimensional force sensors have substantially improved the surgical quality of surgical robots. The purpose of this paper is to provide a comprehensive review of the literature on the design and application of multi-dimensional force sensors in various surgical robots. The multi-dimensional force sensor can monitor the interaction force between the surgical instrument and the soft tissue, enabling doctors to obtain force feedback information in real time during surgery. This can help them improve the accuracy and safety of surgery. The review identifies the importance of sensors classification and performance parameters in surgery for multi-dimensional force measurement. This review focuses on the integrated design of force sensors and multi-dimensional force decoupling algorithms. In addition, a fiber bragg grating multi-dimensional force sensor for puncture surgery was designed to validate the effectiveness of the proposed method. This review paper provides a comprehensive classification and analysis of multi-dimensional force sensors for surgical robots, which will help researchers to understand the integrated design solutions for different surgical instruments and multi-dimensional force sensors.

More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most important problems in current satellite image processing. Due to the complexity and variability of satellite images, especially when the ground is covered with snow, the boundary information of cloud regions is difficult to be accurately identified. The fast and accurate segmentation of cloud regions is a difficult point in the current research. We propose a lightweight convolutional neural network. Firstly, channel attention is used to optimize the effective information in the feature maps as a way to improve the network’s ability to extract semantic information at each scale. Then, we fuse high and low-dimensional feature maps to enhance the network’s ability to obtain small-scale semantic information. In addition, the feature aggregation module automatically adjusts the input multi-level feature weights to highlight the details of different features. Finally, we design the fully connected conditional random field to solve the problem that some noise in the input image and local minima during training is passed to the output layer resulting in the loss of edge features. Experimental results show that the proposed method achieves 0.9695 and 0.8218 for overall accuracy and recall, respectively, which has higher segmentation accuracy with the shortest time consumption compared with other state-of-the-art methods.

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

In the automatic collection of blood from the fingertip, it is necessary to squeeze the finger to increase bleeding due to the relatively tiny blood vessels and the comparatively small blood volume. Thus, this will cause cell fluid to enter the blood extracted and lead to inaccurate test results. If the vein-dense area of the finger is selected as the blood collection point, the amount of blood that can be extracted will be greatly increased. Due to the poor contrast of the infrared image of finger veins, it is difficult to effectively distinguish the vein area with the traditional segmentation method. Deep learning has made great achievements in image segmentation. However, due to the insufficient dataset of infrared image segmentation of finger veins, deep learning has hardly been applied in the image segmentation of finger veins. In order to solve the above problems, we created the dataset of the infrared image segmentation of finger veins and investigated how to use pyramid structure and mechanism of attention to segment the image of finger veins. We used a locally similar pyramid module to quantify a pixel to a similar degree of other pixels, adopted pyramid fusion module to enhance the obtained multiscale features, and proposed a network integrating distinct features of different weights, the details of which were highlighted. Based on the experimental results, this network shows a better performance compared with other advanced methods. It can better segment the finger vein region and, thereby, ensure the accuracy of blood sampling location.