Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks

Accurate localization for mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. Also the proportion factor of distance is proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. And the normalized nearness degrees are considered as the weighted standards to calculate coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can timely localize the mobile node. The average localization error of PCFL can decline by about 30.4% than the AFLA method.Comment: 18 pages, 11 figures, 2 table

Iunius : a cross layer peer-to-peer system with device-to-device communications

Device-to-device (D2D) communications utilizing licensed spectrum have been considered a promising technology to improve cellular network spectral efficiency and offload local traffic from cellular base stations (BSs). In this paper, we develop Iunius: a peer-to-peer (P2P) system based on harvesting data in a community utilizing multi-hop D2D communications. The Iunius system optimizes D2D communications for P2P local file sharing, improves user experience, and offloads traffic from the BSs. The Iunius system features cross-layer integration of: 1) a wireless P2P protocol based on the BitTorrent protocol in the application layer; 2) a simple centralized routing mechanism for multi-hop D2D communications; 3) an interference cancellation technique for conventional cellular (CC) uplink communications; and 4) a radio resource management scheme to mitigate the interference between CC and D2D communications that share the cellular uplink radio resources while maximizing the throughput of D2D communications. Simulation results show that the proposed Iunius system can increase the cellular spectral efficiency, reduce the traffic load of BSs, and improve the data rate and energy saving for mobile users

Microfibrillar-associated protein 5 suppresses adipogenesis by inhibiting essential coactivator of PPAR gamma

Femoral head necrosis is responsible for severe pain and its incidence is increasing. Abnormal adipogenic differentiation and fat cell hypertrophy of bone marrow mesenchymal stem cells increase intramedullary cavity pressure, leading to osteonecrosis. By analyzing gene expression before and after adipogenic differentiation, we found that Microfibril-Associated Protein 5 (MFAP5) is significantly down-regulated in adipogenesis whilst the mechanism of MFAP5 in regulating the differentiation of bone marrow mesenchymal stem cells is unknown. The purpose of this study was to clarify the role of MAFP5 in adipogenesis and therefore provide a theoretical basis for future therapeutic options of osteonecrosis. By knockdown or overexpression of MFAP5 in C3H10 and 3T3-L1 cells, we found that MFAP5 was significantly down-regulated as a key regulator of adipogenic differentiation, and identified the underlying downstream molecular mechanism. MFAP5 directly bound to and inhibited the expression of Staphylococcal Nuclease And Tudor Domain Containing 1, an essential coactivator of PPAR gamma, exerting an important regulatory role in adipogenesis.Peer reviewe

Research on Curriculum Design of 'Real-time Analysis and Design' Based on Multi-core Platform

The change of microprocessor to "multi-core" is not only a new start of the healthy progress of the processor technology, but also a milestone of the development history of computer technology. Naturally, curriculums which are closely related to the hardware will be adjusted with the appearance of "multi-core". In this paper, we introduced the practice and experience of curriculums design based on multi-core platform in the postgraduate and senior undergraduate course-"Real-time system analyses and design"

A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation

A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation