A Few-Shot Modulation Recognition Method Based on Pseudo-Label Semi-Supervised Learning

In order to solve the problem of insufficient labeled samples in modulation recognition, this paper proposes a few-shot modulation recognition algorithm based on pseudo-label semi-supervised learning (pseudo-label algorithm). First of all, high quality artificial feature, excellent classifier and data-labeling method are used to build efficient pseudo label system, and then the pseudo label system is combined with signal classification method based on the deep learning to realize the modulation classification under the condition of a small number of labeled samples and a large number of unlabeled samples. The simulation results show that the pseudo-label algorithm can improve the model recognition performance by 5%-10% when the six kinds of digital signals are classified and identified and its SNR is greater than 5 dB. At the same time, the algorithm has a simple network design and is of great application value.

A new algorithm for finding the K shortest paths in a time-schedule network with constraints on arcs

Time-schedule network with constraints on arcs (TSNCA) means network with a list of pre-defined departure times for each arc. Compared to past research on finding the K shortest paths in TSNCA, the algorithm in this paper is suitable for networks having parallel arcs with the same direction between two nodes. A node label algorithm for finding the K shortest paths between two nodes is proposed. Temporal-arcs are put into the labels of nodes and arranged by ascending order. The number of temporal-arcs is limited to K in every label of node to improve the effectiveness of the algorithm. The complexity of this algorithm is [Formula: see text], where [Formula: see text] is the maximum number of departure times from a node, [Formula: see text] is the number of arcs in network, and [Formula: see text] is the number of nodes in network. Experiments are carried out on major part of real urban mass transit network in Beijing, China. The result proves that the algorithm is effective and practical.

Color Image Segmentation Based on Secondary Watershed and GrowCut Algorithm

GrowCut algorithm is not only an interactive algorithm on the basis of cell automata, but also a multi-label algorithm based on seeds point. Aiming at the GrowCut algorithm usually asks users to partition foreground and background manually and mark a lot more initial seeds. This paper presents an automatic object segmentation method which combining secondary watershed and GrowCut algorithm, here in the following paper refers it to as SWGC algorithm. It firstly using the twice used watershed algorithm to partition the input image, the segmented regions are labeled using Mahalanobis distance, and merged according to the image color and space information, thereafter applying the GrowCut algorithm to perform globally optimized segmentation. The main contribution focuses on performing automatic segmentation which consist of obtain the foreground and background region and generate the seed template of GrowCut algorithm automatically. Thus not only leave out the constraints of user interaction operation, but also avoid the subjectivity and uncertainty. The proposed method reduces the runtime significantly as well as improves the segmentation accuracy and robustness of GrowCut algorithm. Experimental results show SWGC algorithm has superior performance compared to the other related methods.