14385690
A New Least Squares Subspace Projection Approach to Unmix Hyperspectral Data
Linearly constrained discriminant analysis (LCDA) and orthogonal subspace projection (OSP) are both explored in hyperspectral image classification and have shown promise in signature detection, discrimination and classification. However, the two subspace projection approaches cannot directly estimate the signature abundance. The OSP has been extended by a least squares orthogonal subspace projection (LSOSP) to estimate the signature abundance while LCDA has not. The solution of LCDA turns out to be a constrained version of OSP implemented with a data whitening process and the means of samples as signatures. Due to this fact, following the same idea for extending OSP to LSOSP, in this paper, a modified linearly constrained discriminant analysis (MLCDA) is proposed for unmixing hyperspectral data, which can directly estimate the signature abundance. Experiment results obtained from both artificial simulated and practical remote sensing data demonstrate that the MLCDA algorithm performs better than least squares method and the LSOSP.
35977333
Multi-Media Retrieval with Semantic Web: A Case Study in Airport Security Inspection Applications
Multi-media retrieval based on content meaning is an important issue. It has significant applications in the tracing of terrorists based on airport security inspection information. In this paper, we review the defects of traditional image retrieval technology. A semantic Web based image retrieval method is proposed, which is deployed into an airport security inspection information retrieval system. We present the techniques of domain ontology construction and semantic-Web annotation for the media. An image retrieval technique based on semantic annotation is suggested, and the design of a semantic information retrieval prototype system for airport security inspection application is described.
26319937
Intelligent Metal Powder Laser Forming System
Metal Power Laser Forming (MPLF) is a high precision and complicated process. The quality of the formed parts is affected by many parameters and may vary significantly during the laser forming process. In order to guarantee the stabilities of the process, a state-of-the-art intelligent MPLF system was developed in this paper. The intelligent system includes CNC system, energy supply and control system, powder delivering and control system, and realtime feedback control system. The established state-of-the-art apparatus and several metal parts formed by the MPLF system are presented in this paper.
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
14078787
Maximum Margin Clustering Made Practical
Motivated by the success of large margin methods in supervised learning, maximum margin clustering (MMC) is a recent approach that aims at extending large margin methods to unsupervised learning. However, its optimization problem is nonconvex and existing MMC methods all rely on reformulating and relaxing the nonconvex optimization problem as semidefinite programs (SDP). Though SDP is convex and standard solvers are available, they are computationally very expensive and only small data sets can be handled. To make MMC more practical, we avoid SDP relaxations and propose in this paper an efficient approach that performs alternating optimization directly on the original nonconvex problem. A key step to avoid premature convergence in the resultant iterative procedure is to change the loss function from the hinge loss to the Laplacian/square loss so that overconfident predictions are penalized. Experiments on a number of synthetic and real-world data sets demonstrate that the proposed approach is more accurate, much faster (hundreds to tens of thousands of times faster), and can handle data sets that are hundreds of times larger than the largest data set reported in the MMC literature.
5848822
Soft decoder architecture of LT codes
Luby transform (LT) codes, as the first class of efficient rateless codes, attract a lot of attention in the coding theory field. However, the VLSI implementation of LT codes is challenging due to its random code construction characteristic as well as the flexible output length. In this paper, we present an applicable architecture of a soft-decision LT decoder with a block length of 1024 bits and 100 iterations. Partly parallel input node processing and output node processing techniques are both adopted to accelerate decoding speed. An efficient router and reverse router are designed to indicate the graphic connectivity between input nodes and output nodes. The parallel architecture is prototyped on the target FPGA device.
9437535
Practical Application of FDC in Software Service Pricing
This paper proposes a modified fully distributed cost (FDC) pricing method with third-degree price discrimination for software service. The method is a feasible and simple method to be used in practice. The pricing process is generalized as involving data collection, strategy analysis and strategy establishment. Then a practical SCM software service pricing in retail industry is described
10126460
Mining common topics from multiple asynchronous text streams
Text streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a large volume of data. An effective way to explore the semantic as well as temporal information in text streams is topic mining, which can further facilitate other knowledge discovery procedures. In many applications, we are facing multiple text streams which are related to each other and share common topics. The correlation among these streams can provide more meaningful and comprehensive clues for topic mining than those from each individual stream. However, it is nontrivial to explore the correlation with the existence of asynchronism among multiple streams, i.e. documents from different streams about the same topic may have different timestamps, which remains unsolved in the context of topic mining. In this paper, we formally address this problem and put forward a novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step extracts common topics from multiple streams based on the adjusted timestamps by the second step; the second step adjusts the timestamps of the documents according to the time distribution of the discovered topics by the first step. We perform these two steps alternately and a monotone convergence of our objective function is guaranteed. The effectiveness and advantage of our approach were justified by extensive empirical studies on two real data sets consisting of six research paper streams and two news article streams, respectively.