A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks

In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance

Diagnosis and Exercise Rehabilitation of Knee Joint Anterior Cruciate Ligament Injury Based on 3D-CT Reconstruction

The joint capsule of the knee joint is attached to the edges of various articular surfaces and is thin and loose. Therefore, ligament reinforcement is needed to protect the knee joint and increase the stability of the joint. It plays a vital role in human activities. In this paper, a 3D-CT three-dimensional reconstruction method is used to reconstruct the ACL natural femoral imprint and double-bone tract. The relative positional relationship between the two center points is compared, and the law is summarized to guide the improvement of ACL anatomic double-beam reconstruction under arthroscopy. The 3D reconstruction results suggest that the bone layer in the anterior medial portion is the thickest, forming a peak, and the thickness of the bone layer in the posterior medial portion gradually decreases in a stepwise manner. The entire bone tissue in the anterior medial portion and posterior medial portion is integrated into one body. The tissues are connected as a whole, and the thickness is relatively uniform. The two parts of the bone tissues are not connected. The CF tissue was inserted into the bone tissue in a zigzag pattern. The changes of CF tissues in the anterior medial and posteromedial CF tissues were similar, and they were distributed stepwise from the inside to the outside. According to the bone and CF spatial structure and changing rules, ACL is divided into medial and lateral beams. According to this study, it can be summarized that (1) 3D reconstruction can clearly reconstruct the natural footprint of ACL femoral stops and postoperative osseous position and (2) 3D reconstruction can be used to evaluate the position of osseous postoperative ACL anatomic double-beam reconstruction. Arthroscopy double-beam reconstruction of ACL is instructive

A Spectrum Sensing Method Based on Empirical Mode Decomposition and K-Means Clustering Algorithm

To solve the problems of poor performance of traditional spectrum sensing method under low signal-to-noise ratio, a new spectrum sensing method based on Empirical Mode Decomposition algorithm and K-means clustering algorithm is proposed. Firstly, the Empirical Mode Decomposition algorithm and the wavelet threshold algorithm are used to remove the noise components in the spectrum sensing signal, and K-means clustering algorithm is used to determine whether the primary user exists. The method can remove the redundant components such as noise in the nonstationary or nonlinear sampling signal in the real environment and does not need to know the prior information such as signal, channel, and noise, so it can well handle the complicated sensing signal in real environment. This method can reduce the impact of noise on the spectrum sensing system and thus can improve the sensing performance of the system. In the experimental part, the difference between maximum and minimum eigenvalues and the difference between the maximum eigenvalue and the average energy in the random matrix are selected as signal features. Experiments also show that the proposed method is better than the traditional spectrum sensing methods

En quoi l'universitarisation de la formation infirmière modifie-t-elle la transmission ?

Atelier 21 : Formation aux métiers du soinL'universitarisation de la formation infirmière est débutée depuis trois ans (2009) avec son corollaire qui est la mise en place d'un référentiel infirmier basé sur le développement des compétences. Nous avons choisi de mettre en exergue une pratique professionnelle incontournable, celle de la transmission. Cette recherche, effectuée en janvier 2012, s'articule autour d'une question centrée sur les attendus de compétences infirmières. L'interrogation de vingt professionnels de santé sur leurs représentations des transmissions permet d'en voir la pluralité de significations comme des manières de faire et la place qu'ils accordent à l'université dans le processus de professionnalisation. Les résultats porteront sur l'analyse des discours des différents professionnels croisés avec les référentiels d'activités, de compétences et de formation infirmière

Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model

Spectrum sensing is a core technology in cognitive radio (CR) systems. In this paper, a multiple-antenna cooperative spectrum sensor based on the wavelet transform and Gaussian mixture model (MAWG) is proposed. Compared with traditional methods, the MAWG method avoids the derivation of the threshold and improves the performance of single secondary user (SU) spectrum sensing in cases of channel loss and hidden terminal. The MAWG method reduces the noise of the signal which collected by the multiple-antenna SUs through the wavelet transform. Then, the fusion center (FC) extracts the statistical features from the signals that are pre-processed by the wavelet transform. To extract the statistical features, an sensing data fusion method is proposed. The MAWG method divides all SUs that are involved in the cooperative spectrum sensing into two clusters and extracts a two-dimensional feature vector. In order to avoid complicated decision threshold derivation, the Gaussian mixture model (GMM) is used to train a classifier for spectrum sensing according to these two-dimensional feature vectors. Simulation experiments are performed in the κ − μ channel model. The simulation shows that the MAWG can effectively improve spectrum sensing performance under the κ − μ channel model

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

Abstract To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance

Genetic mechanism and permeability evaluation of low contrast oil reservoirs in M Oilfield of Wushi Sag

The sandstone body of the main oil layer of M oilfield in Wushi Sag is large, and the lithology is mainly gravelly medium-coarse sandstone and sandy conglomerate, followed by fine sandstone.Due to the influence of special sedimentation and diagenesis, the shallow buried oil layer shows the characteristics of high resistivity, while the resistivities of some middle and deep buried oil layers are very close with that of the water layers, which brings great difficulties to the well logging based identification of fluid properties and quantitative evaluation of permeability.Based on the experimental data of NMR, mercury injection and cast thin sections, this paper analyzed the difference in reservoir characteristics between high resistivity reservoirs and low contrast reservoirs from the microscopic view.Then, based on the pore types (intergranular pores, mixed pores and mold pores), the resevoirs were diveded into three types, and the reservoir type classification standard were established. At last, the reservoir type-based permeability estimation models were proposed.The results show that the high irreducible water saturation caused by the complexity of pore structure is the main reason of the low contrast reservoirs. The accuracy of the estimated permeability is significantly improved after reservoir classification, which lays a solid foundation for the formulation and implementation of the oilfield development plan and post drilling evaluation