In recent years, deep convolutional neural networks (CNN) have been widely used in synthetic aperture radar (SAR) image recognition. However, due to the difficulty in obtaining SAR image samples, training data is relatively few and overfitting is easy to occur when using traditional CNNS used in optical image recognition. In this paper, a CNN-based SAR image recognition algorithm is proposed, which can effectively reduce network parameters, avoid model overfitting and improve recognition accuracy. The algorithm first constructs a convolutional network feature extractor with a small size convolution kernel, then constructs a classifier based on the convolution layer, and designs a loss function based on distance measurement. The networks are trained in two stages: in the first stage, the distance measurement loss function is used to train the feature extraction network; in the second stage, cross-entropy is used to train the whole model. The public benchmark dataset MSTAR is used for experiments. Comparison experiments prove that the proposed method has higher accuracy than the state-of-the-art algorithms and the classical image recognition algorithms. The ablation experiment results prove the effectiveness of each part of the proposed algorithm.

This paper presents a new generalized single threshold model that can be used in communications and cellular networks. In the proposed model, called Single Hysteresis Model (SHM), it is assumed that the amount of resources accessible for a new call of a given class can depend on two load areas of the system. The switching between areas is modulated by the two-state Markov chain which determines the average time the system spends in a particular load area, i.e. the area in which calls of selected classes with a reduced amount of resources (high load area) and with the initial amount of resources (low load area) are serviced. The results obtained for the discussed analytical model are compared with the results of the simulation of an exemplary WCDMA radio interface carrying a mixture of different multi-rate traffic streams. The research study confirms high accuracy of the proposed model.

With the existing of coverage holes, the Quality of Service (such as event response, package delay, and the life time et al.) of a Wireless Sensor Network (WSN) may become weaker. In order to recover the holes, one can locate them by identifying the boundary nodes on their edges. Little effort has been made to distinguish the boundary nodes in a model where wireless sensors are randomly deployed on a three-dimensional surface. In this paper, we propose a distributed method which contains three steps in succession. It first projects the 1-hop neighborhood of a sensor to the plane. Then, it sorts the projected nodes according to their angles and finds out if there exists any ring formed by them. At last, the algorithm validates a circle to confirm that it is a ring surrounding the node. Our solution simulates the behavior of rotating a semicircle plate around a sensor under the guidance of its neighbors. Different from the existing results, our method transforms a three-dimensional problem into a two-dimensional one and maintaining its original topology, and it does not rely on any complex Hamiltonian Cycle finding to test the existence of a circle in the neighborhood of a sensor. Simulation results show our method outperforms others at the correctness and effectiveness in identifying the nodes on the edges of a three-dimensional WSN.

This paper presents a new, accurate multi-service model of a queueing system with state-dependent distribution of resources for each class of calls. The analysis of the considered queueing system was carried out at both the microstate and macrostate levels. The proposed model makes it possible to evaluate averaged parameters of queues for individual classes of calls that are offered to the system. In addition, the paper proposes a new algorithm for a determination of the occupancy distribution in the queueing system at the microstate level. The results of the calculations are compared with the results of a digital simulation for multi-service queueing systems with state-independent distribution of resources.