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Due to the uncertainty of the Radio Frequency Identification (RFID) measurements and limit of the placement of the readers, it's necessary to use the estimation method to obtain more accurate trajectory in RFID indoor tracking system. The traditional recursive estimation from K to K+1 sampling point may fail, because the measurement of RFID system is irregular sampling due to the data-driven measurement...
A novel multi-focus image fusion method based on Uniform Discrete Curvelet Transform (UDCT) is proposed to overcome conventional muti-scale analysis image fusion shortcomings, such as high data redundancy ratio, complicated structure and poor performance, etc. First, UDCT is applied to the multi-focus images and subband coefficients of multi-scales and multi-directions are obtained. Then, different...
Spectrum Sensing is a cornerstone in cognitive radio which can detect the spectrum holes in order to raise spectrum utilization ratio. Traditional spectrum sensing detectors depend on some prior information or are restricted by low signal-to-noise ratio and computation complexity in practical application. A GoDec based spectrum sensing detector is proposed by combining covariance based method with...
With the fast development of international internet and information technology, the contradiction between the personal information circulation and the personal information legal protection is getting more and more serious. In order to solve the problem about the personal information circulation and its legal protection, firstly catch the meaning of the personal information circulation and its legal...
In order to promote the high frequency performance of open architecture CNC system, an embedded CNC controller combining FPGA (Field Programmable Gate Array) technology and real-time Ethernet communication bus was designed, which consisted of an embedded ARM (Advanced RISC Machines) processor and an FPGA. An open source CNC software running under Linux operating system was customized to accommodate...
In order to achieve a reasonable evaluation of direct trust, this paper proposes a trust evaluation algorithm based on the domain, using the technique of constructing a hierarchical tree of trust evaluation subjectively. The algorithm adopts the rules of series and parallel operations in the D-S theory, acquires the results of the recommended trust problem of a single path by quadrature methods and...
Endowing users of multi-interface mobile handsets the competence to seamlessly roam among diverse heterogeneous wireless networks has become a crucial challenge confronting the network operators / service providers in the recent years. Today, we have moved far beyond the 3G communication networks, wherein, potential to handover traditionally relied on the channel quality computed from the received...
For the multisensor linear discrete time-invariant stochastic system with unknown noise variances, the new measurement system is constructed by using matrix pseudo-inverse method, which can yield many groups of new measurement sequences by cooperating work. Furthermore, the statistics characteristics of the new measurement sequences are analyzed to determine whether the sensors are faulting or not...
It is well known that estimation performance of the Kalman filtering (KF) depends closely on systemic observability. Moreover, observable degree is usually used to measure the ability of observability on systemic state variables in control and estimation systems. Thereby, there should be a corresponding relation between the estimation performance of the KF and the observable degree. Unfortunately,...
Based on fisher ratio class separability measure, we propose two types of posterior probability support vector machines (PPSVMs) using binary tree structure. The first one is a some-against-rest binary tree of PPSVM classifiers (SBT), for which some classes as a cluster are divided from the rest classes at each non-leaf node. To determine the two clusters, we use the Fisher ratio separability measure...
In order to obtain more accurate state estimation from multisensor system, the state estimation with two-level fusion structure is presented. By means of measurement fusion algorithm, the local first-level fusion centers can obtain the globally optimal fused measurement information, and then the local state estimation can be got by classical Kalman filtering. At the second-level fusion center, the...
Pansharpening has been an important tool in remote sensing applications, which transforms a set of low-spatial-resolution multispectral images to high-spatial-resolution images by fusing a co-registered fine-spatial-resolution panchromatic image. The new style very high-resolution WorldView-2 satellite images have posed challenges to the image fusion techniques. An effective pansharpening method based...
This paper studies the distributed fusion filtering problem for multi-sensor stochastic systems with unknown inputs and one-step random delays. By defining some new variables, the original system with unknown inputs and random delays is equivalently transformed into a stochastic parameterized system. The time-delay is depicted by a Bernoulli distributed random variable. No prior information about...
For the multisensor time-invariant uncertain system with uncertainties of both parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, the uncertain system can be converted into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle, and weighted least squares method, a robust weighted measurement...
This paper presents a deep data fusion model for risk perception and coordinated control in a regional power system control center. A knowledge learning data fusion approach has been used to find an efficient state representation based on prior knowledge from cross-domain energy management systems. In particular, a kernel principal components analysis technique is presented for nonlinear dimensionality...
For the linear discrete time multisensor system with uncertain model parameters and noise variances, the centralized fusion robust steady-state Kalman filter is presented by a new approach of compensating the parameter uncertainties by a fictitious noise. Based on the minimax robust estimation principle, a robust centralized fusion Kalman filter is presented based on the worst-case conservative systems...
Text detection and recognition in natural scene images plays an important role in content analysis of images. In this paper, based on the characteristics of scene text, we propose a robust text detection and recognition method using Maximally Stable Extremal Regions (MSER) and Support Vector Machine (SVM). Different from the end to end text recognition, we split the recognition problem into detection...
Convolution-based detection models (CDM) have achieved tremendous success in computer vision in last few years, such as deformable part-based models (DPM) and convolutional neural networks (CNN). The simplicity of these models allows for very large scale training to achieve higher robustness and recognition performance. However, the main bottleneck of those powerful state-of-the-art models is the...
In this paper, transform domain LMS (TDLMS) and TDLMS based on decomposition technology (TDLMS-DT) are mixed together by so-called convex combination approach to achieve relatively fast convergence speed and low steady-state performance. The simulation results confirm the efficiency of the proposed algorithm.
In this paper, we present an efficient algorithm for sparse signal recovery with high exact recovery rate. The main idea of the algorithm is to combine two existing methods: linearized Bregman algorithm and reweighting technique. Compared with other available methods, such as reweighted Basis Pursuit (BP) and linearized Bregman, the proposed algorithm has a much lower computational complexity with...
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