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In underwater wireless sensor networks (USWNs), localizing unknown nodes is essential for most applications while is more complex than that of terrestrial WSNs. In this paper, we propose a range-based localization scheme using deep neural network (DNN). Numerical results suggest that the proposed DNN localization algorithm outperforms traditional schemes using least squares support vector machines...
Due to the growth prospect of analog circuit fault diagnosis, this paper tends to introduce a novel arithmetic model based on least squares support vector machine (LSSVM) and the semi-supervised learning (SSL) scheme which is adept at cost-saving. The proposed method contains two steps. Firstly, the fact that large deviation may emerge as a result of the empirical risk inspires the idea of an improved...
This paper presents a new approach to recognize the types of moving vehicles in a distributed, wireless sensor network, based on sparse signal representation. Through a sparse representation computed by l1-minimization, we propose a general classification algorithm for acoustic object recognition. This algorithm first uses Mel frequency cepstral coefficients to extract the acoustic features of vehicles...
This paper proposes a novel approach based on scale invariant feature transform (SIFT) and kernel sparse representation for traffic sign recognition in complex traffic scenes. This module consists of several steps. In the first stage, SIFT is introduced for feature extraction from samples and test targets, respectively. The features are mapping to the kernel space. In the second stage, we construct...
Hyperspectral image classification is difficult due to the high dimensional features, high intraclass variance, low interclass variance but limited training samples. In this paper, the ECASSL (Ensured Collaborative Active and Semi-Supervised Labeling) approach, which attempts to exploit pseudo-labeled samples to improve the performance of active learning based hyperspectral image classification, is...
Adaptive modulation technique has been widely used in wireless communication systems and channel prediction plays an important role in adaptive modulation technique. Minimax probability machine shows good performance in classification and prediction by controlling the generalization error boundary and trying to make it lowest. In this paper, we introduce a nonlinear prediction algorithm of fast fading...
In order to arising the safety and reliability, and monitoring the working states of numerical control system, aiming at the multi-kinds of potential connection-related faults, the construction and the principle of the system were analyzed, and the systemic diagnosis framework was developed. Using the position signal and the torque monitoring one, the parameters of support vector machine were trained...
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