In heterogeneous wireless sensor networks (HWSN), sensor nodes may fail due to running out of battery power or sudden damage. This causes HWSN being partitioned to a number of separate networks. Relay node placement is to add new relay nodes to partitioned HWSN such that the network recovers to wireless communication. Energy efficient routing in HWSN is useful for prolonging network lifetime and energy conservation. This paper attempts to solve relay node placement and energy efficient routing problems for HWSN. Both problems have seldom been studied together in the literature. This paper first constructs a mathematical model for both problems. For relay node placement problem, it is assumed that HWSN contains unreachable area, where sensor nodes could not be placed. For energy efficient routing, it is transformed to path length of wireless communication. As the problem is non-deterministic polynomial (NP) hard, a heuristic method called whale optimizer is used. The paper studies the effect of whale optimizer method with three adaptive schemes. Numerical simulations are done to test the proposed method for HWSN. The analysis and discussion show that the proposed method is useful to address NP hard relay node placement and energy saving problems for HWSN.

Effect of Polysaccharides from Bletilla striata on the Healing of Dermal Wounds in Mice: Bletilla striata has been largely used in traditional folk medicine in China as a wound healing agent and to treat gastritis and several other health problems. Some studies have shown that plant polysaccharides may have the ability to promote wound healing. The aim of this work was to evaluate the wound healing activity of the polysaccharide extracted from Bletilla striata. Firstly, a Bletilla striata polysaccharide was extracted by water extraction and alcohol precipitation and characterized by Fourier transform infrared spectroscopy. The Bletilla striata polysaccharide was then tested for cell migration and proliferation using the mouse fibroblast cell line. Then, the Bletilla striata hydrogel was fabricated for acute wound health care of the mouse full-thickness excision. The results showed that the BSP enhanced the proliferation and migration of L929 cells. The superior wound healing capacity of the BSP hydrogel was demonstrated that it significantly accelerated the wound healing process in vivo in full-thickness skin defect wounded models. Compared to the saline group, the BSP hydrogel could accelerate wound healing and promote re-epithelialization and collagen deposition by means of TGF-β/Smad signal pathway activation. Taken together, BSP hydrogel would be a useful pharmaceutic candidate for acute cutaneous wound health care.

Anomaly detection in big data is a key problem in the big data analytics domain. In this paper, the definitions of anomaly detection and big data were presented. Due to the sampling and storage burden and the inadequacy of privacy protection of anomaly detection based on uncompressed data, compressive sensing theory was introduced and used in the anomaly detection algorithm. The anomaly detection criterion based on wavelet packet transform and statistic process control theory was deduced. The proposed anomaly detection technique was used for through-wall human detection to demonstrate the effectiveness. The experiments for detecting humans behind a brick wall and gypsum based on ultra-wideband radar signal were carried out. The results showed that the proposed anomaly detection algorithm could effectively detect the existence of a human being through compressed signals and uncompressed data.

Hyperspectral transmission imaging may provide a means for rapid screening of breast tumors, but tissue has a strong nature of scattering, thus causing great difficulty in identifying heterogeneity. In this paper, a combination of frame accumulation and deep learning was proposed to detect heterogeneity, and we designed the simulation experiment of collecting phantom images. On the basis of frame accumulation preprocessing, heterogeneous detection is performed on multispectral images by using Faster R-CNN (Faster Regions with Convolutional Neural Networks features) and SSD (Single Shot MultiBox Detector), two typical detection frameworks of deep learning. The results show that the mAP of Faster R-CNN and SSD reach 90.8% and 95.1% respectively when 3 classes (including background) are detected with the help of the dataset provided in the paper, and the mAP (mean Average Precision) of two frameworks both reach 99.9% when 2 classes (including background) are detected. The detection efficiency of SSD is higher than Faster, SSD’s detection speed can reach 50fps, and the detection accuracy of the images after frame accumulation preprocessing is higher than that without frame accumulation processing. In summary, we validate the possibility of employing Faster R-CNN and SSD to detect heterogeneity in multispectral images based on frame accumulation that improves image grayscale resolution, and it has a certain degree of reference significance for the application of deep learning in multispectral image detection.

The technology for through-wall human detection with ultra-wideband (UWB) radar was discussed. Due to the large amount of UWB radar data, compressive sensing theory was introduced and compressed UWB radar data can be collected. The singular value decomposition algorithm was used to acquire the singular values of compressed radar data. The compressed UWB radar data were collected at two statuses of human being for gypsum wall. The experimental results showed that the singular values with a human target were increased compared with those without a target.