A Holistic Approach To Reconstruct Data In Ocean Sensor Network Using Compression Sensing

In the complex marine environment, a large-scale wireless sensor network (WSN) is often deployed to resolve the sparsity issue of the signal and to enforce an accurate reconstruction of the signal by upgrading the transmission efficiency. To best implement, such a WSN, we develop a holistic method by considering both raw signal processing and signal reconstruction factors: A node re-ordering scheme based on compression sensing and an improved sparse adaptive tracking algorithm. First, the sensor nodes are reordered at the sink node to improve the sparsity of the compression sensing algorithm in the discrete cosine transformation or Fourier transform domain. After that, we adopt the matching test to estimate sparse degree Kis. At last, we develop a sparse degree adaptive matching tracking framework step-by-step to calculate the approximation of sparsity, and ultimately converge to a precise reconstruction of the signal. In this paper, we employ MATLAB to simulate the algorithm and conduct comprehensive tests. The experimental results show that the proposed method can effectively reduce the sparsity of the signal and deliver an accurate reconstruction of the signal especially in the case of unknown sparsity

Assessing spatial vulnerability from rapid urbanization to inform coastal urban regional planning

This study delves into the development of a Geographic Information System (GIS) based vulnerability assessment tool for assessing coastal vulnerability and making prescriptive recommendations on urban planning in coastal regions at a local level. The framework of “exposure-sensitivity-resilience” (ESR) is not only applied, but also improved and refined to take into account a suite of social-ecological indicators. The results demonstrate that vulnerability was not evenly distributed across Haikou\u27s coastal zones, which may be linked to the different stages of ongoing urban planning for coastal Haikou. For the case study areas, vulnerability tends to increase with higher levels of urbanization, but may decrease once the speed of urban expansion is under control. The most vulnerable area is the main city zone where urban residents are concentrated and a developed transportation network exists. Our study contributes to the development of a general methodology to assess vulnerability in rapid urbanization and to apply it to coastal cities around the world

A Holistic Approach to Reconstruct Data in Ocean Sensor Network Using Compression Sensing

In the complex marine environment, a large-scale wireless sensor network (WSN) is often deployed to resolve the sparsity issue of the signal and to enforce an accurate reconstruction of the signal by upgrading the transmission efficiency. To best implement, such a WSN, we develop a holistic method by considering both raw signal processing and signal reconstruction factors: A node re-ordering scheme based on compression sensing and an improved sparse adaptive tracking algorithm. First, the sensor nodes are reordered at the sink node to improve the sparsity of the compression sensing algorithm in the discrete cosine transformation or Fourier transform domain. After that, we adopt the matching test to estimate sparse degree Kis. At last, we develop a sparse degree adaptive matching tracking framework step-by-step to calculate the approximation of sparsity, and ultimately converge to a precise reconstruction of the signal. In this paper, we employ MATLAB to simulate the algorithm and conduct comprehensive tests. The experimental results show that the proposed method can effectively reduce the sparsity of the signal and deliver an accurate reconstruction of the signal especially in the case of unknown sparsity

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system

An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been extensively applied in ecological environment monitoring. Typically, event boundary detection is an effective method to determine the scope of an event area in large-scale environment monitoring. This paper proposes a novel lightweight Entropy based Event Boundary Detection algorithm (EEBD) in WSNs. We first develop a statistic model using information entropy to figure out the probability that a sensor is a boundary sensor. The EEBD is independently executed on each wireless sensor in order to judge whether it is a boundary sensor node, by comparing the values of entropy against the threshold which depends on the boundary width. Simulation results demonstrate that the EEBD is computable and offers valuable detection accuracy of boundary nodes with both low and high network node density. This study also includes experiments that verify the EEBD which is applicable in a real ocean environmental monitoring scenario using WSNs

Integrative Analysis of Angiogenesis-Related Long Non-Coding RNA and Identification of a Six-DEARlncRNA Signature Associated with Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is a lethal gastrointestinal malignancy worldwide. We aimed to identify an angiogenesis-related lncRNAs (ARlncRNAs) signature that could predict the prognosis in ESCC. The GSE53624 and GSE53622 datasets were derived from the GEO database. The differently expressed ARlncRNAs (DEARlncRNAs) were retrieved by the weighted gene co-expression network analysis (WGCNA), differential expression analysis, and correlation analysis. Optimal lncRNA biomarkers were screened from the training set and the six-DEARlncRNA signature comprising AP000696.2, LINC01711, RP11-70C1.3, AP000487.5, AC011997.1, and RP11-225N10.1 could separate patients into high- and low-risk groups with markedly different survival. The validation of the reliability of the risk model was performed by the Kaplan-Meier test, ROC curves, and risk curves in the test set and validation set. Predictive independence analysis indicated that risk score is an independent prognostic biomarker for predicting the prognosis of ESCC patients. Subsequently, a ceRNA regulatory network and functional enrichment analysis were performed. The IC50 test revealed that patients in the high-risk group were resistant to Gefitinib and Lapatinib. Finally, the six DEARlncRNAs were detected by qRT-PCR. In conclusion, we demonstrated a novel ARlncRNA signature as an independent prognostic factor to distinguish the risk of ESCC patients and benefit the personalized clinical applications

Molecule-based water-oxidation catalysts (WOCs) : cluster-size-dependent dye-sensitized polyoxometalates for visible-light-driven O2 evolution

From atomic level to understand the cluster-size-dependant behavior of dye-sensitized photocatalysts is very important and helpful to design new photocatalytic materials. Although the relationship between the photocatalytic behaviors and particles' size/shape has been widely investigated by theoretical scientists, the experimental evidences are much less. In this manuscript, we successfully synthesized three new ruthenium dye-sensitized polyoxometalates (POM-n, n relate to different size clusters) with different-sized POM clusters. Under visible-light illumination, all three complexes show the stable O 2 evolution with the efficient order POM-3 > POM-2 > POM-1. This cluster-size-dependent catalytic behavior could be explained by the different numbers of M = Ot (terminal oxygen) bonds in each individual cluster because it is well-known that Mo = Ot groups are the catalytically active sites for photooxidation reaction. The proposed mechanism of water oxidation for the dye-sensitized POMs is radical reaction process. This research could open up new perspectives for developing new POM-based WOCs.Published versio