38592732
Remote Sensing Image Classification Based on Evidence Theory and Neural Networks
An effective method based on evidence theory and neural networks to classify the remote sensing images is brought forward in the paper. Firstly, with the spatial information in consideration, the original image is smoothed with a modified gradient inverse weighting smoothing method, then the classification of the original and smoothed images is performed separately using a B-P neural network. Finally, result comes out after fusing the two classification results with D-S evidence theory. Experimental results demonstrate that the proposed method is effective and can improve the classification accuracy.
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.
16797771
Semi-supervised Multi-label Learning by Solving a Sylvester Equation
Multi-label learning refers to the problems where an instance can be assigned to more than one category. In this paper, we present a novel Semi-supervised algorithm for Multi-label learning by solving a Sylvester Equation (SMSE). Two graphs are first constructed on instance level and category level respectively. For instance level, a graph is defined based on both labeled and unlabeled instances, where each node represents one instance and each edge weight reflects the similarity between corresponding pairwise instances. Similarly, for category level, a graph is also built based on all the categories, where each node represents one category and each edge weight reflects the similarity between corresponding pairwise categories. A regularization framework combining two regularization terms for the two graphs is suggested. The regularization term for instance graph measures the smoothness of the labels of instances, and the regularization term for category graph measures the smoothness of the labels of categories. We show that the labels of unlabeled data finally can be obtained by solving a Sylvester Equation. Experiments on RCV1 data set show that SMSE can make full use of the unlabeled data information as well as the correlations among categories and achieve good performance. In addition, we give a SMSE’s extended application on collaborative filtering.
15229361
A picosecond TDC architecture for multiphase PLLs
This paper presented a time-to-digital converter (TDC) embedded PLL in 3D silicon-on-insulator (SOI) process. The difficulty of tier-to-tier interconnection modeling in 3D process is very critical. The proposed TDC effectively eliminates the phase mismatches introduced by interconnection between different tiers among multiphase clocks in the 3D process. The large process variation presented in the 3D integrated circuits requires digital compensation techniques to accurately control the timing. The TDC structure presented in this paper replaced the traditional long Vernier delay line and achieved a 2ps timing resolution. A feedback structure is implemented into the TDC to eliminate the influence of the PVT variations on the timing resolution. The multiphase PLL frequency is 3.8GHz in this paper. Simulation results show that the clock jitter was decreased from 5.98ps to 3.58ps with the proposed TDC phase calibration circuit which presents a significant decrease of phase mismatches.
37239622
Classifying Motion Time Series Using Neural Networks
This paper proposes an effective time-series classification model based on the Neural Networks. Classification under this model consists of three phases, namely data preprocessing, training, and testing. The main contributions of the paper are described as following: We propose a feature extraction algorithm, which involves computation of finite difference of sequences, for preprocessing. We employ two different types of Neural Networks for training and testing. The results of the experiments on real univariate motion capture data and synthetic data show that our approach is effective in providing good performance in terms of accuracy. It is therefore a promising method for classifying time-series, in particular for univariate human motion capture data.
1048322
Soundness of Coercion in the Calculus of Constructions
A coercive subtyping system for the Calculus of Constructions is presented. The proposed system λC≤co is obtained essentially by adding coercions and η-conversion to λC≤, which is a subtyping extension to the Calculus of Constructions without coercions. We proved that a coercion c:A ≤ B is semantically sound. That is, (1) c is a term of type A → B; (2) c behaves like an identity function. This development follows the spirit of semantical interpretation of subtyping.
8517904
A Method of Video Quality Assessment Based on the Sensitive Region
Zhou Wang proposed the Structural Similarity theory (MSSIM), Guan-Hao Chen proposed a marginal texture distorts theory on it. Jian-Xin Pang proposed a method of image quality assessment based on the energy of structural distortion (ESD). Because the theory isn't applied directly on the video field and it don’t think over HVS. So we propose a method of video quality assessment based on the sensitive region. First of all, we get the energy of structural distortion (ESD) based on the block. Then, we get the weighted factor according to the structure information and the movement information. Finally, we get the final results according to the HVS. From the last results, we know that the weighting method base on the ESD (WESD) is better than the PSNR, MSSIM and ESSIM on the compress losing, smooth losing and noise losing.