Search

The orthogonal frequency division multiplexing( OFDM), widely applied in underwater acoustic( UWA) communication,is an efficiently multicarrier modulation technique.But OFDM requires the subcarriers to be orthogonal and is very sensitive to frequency offset. The Doppler compensation or adding the subcarrier filter is mainly used to combat Doppler shift in UWA communication,but the implementation is complicated. This paper investigates the method of reducing intercarrier interference( ICI) from a new perspective,that is,increasing the spacing between subcarriers. The combined frequency division multiplexing( C-FDM) is proposed and several OFDM subcarrier blocks are combined into one new C-FDM subcarrier block,and the same frequency subcarrier near each other to construct new C-FDM subcarriers is arranged.Assuming the symbol lengths of C-FDM and OFDM are equal and the subcarrier spacing of C-FDM is larger, the anti-Doppler shifting capability is effectively improved without complex compensation algorithms. The simulation results showed that C-FDM technology has better BER performance in UWA channel. [ABSTRACT FROM AUTHOR]

Current research mainly focuses on the case that a single sensor node sends data, and the transmission efficiency is low. Using multiple sensor nodes to send data at the same time can improve the transmission efficiency, but there is interference between user' s data. Compressed sensing (CS) theory is applied to underwater acoustic sensor networks. A large-capacity cooperative communication method was proposed to realize simultaneous transmission of data by multiple nodes. Using the sparse characteristics of sensor nodes, simultaneous transmission of data from multiple source nodes is equivalent to the measurement process in CS. It is proved that the transmission characteristics of multiple nodes can satisfy the restricted isometry property ( RIP) in the CS theory. The destination node can recover the data of multiple concurrent sensor nodes by the reconstruction algorithm. [ABSTRACT FROM AUTHOR]

Intrusion detection systems are essential to protect the network security. However, it is hard for a traditional single algorithm to attain satisfied detection results for different attack classes. To solve this problem, this paper proposes an intrusion detection method based on Extreme Learning Machine (ELM) and modified K-means. ELM algorithm is optimized by Parametric Rectified Linear Unit (PReLU) activation function. The modified K-means algorithm can automatically select the initial centroids of clusters and the number of clusters by setting the distance threshold. Based on cascade algorithms, a hybrid intrusion detection method is designed based on improved ELM and modified K-means. The experimental results on NSL-KDD dataset show that, compared with other traditional algorithms such as BP neural network, Support Vector Machine (SVM) and ELM,the proposed method improves the detection results and reduces the false alarm rate. [ABSTRACT FROM AUTHOR]