An automatic selection method and its application to key search algorithms

The highway electromechanical system is an important support to ensure normal operation of the highway. Currently, the cyclical and integral maintenance strategy was mainly adopted. In order to improve the equipment utilization and economic performance, a key equipment search algorithm based on complex network structure is presented in this paper, which gives a new way with differentiation for highway electromechanical maintenance. Based on the topological structure of mechanical and electrical system, the centrality, closeness centrality and distance centrality is combined as effective indicators of critical nodes. Moreover, in order to measure the topology of network and importance of neighboring nodes, mutual information of nodes in networks is calculated via the Markov chain interaction algorithm. By analysis on this project, different conditions lead to little difference of the research result, but acquire the same effect for critical nodes analysis. This paper proposes a new method to search the critical nodes in electromechanical system from the perspective of complex network, which provides a new theoretical basis for the reasonable design of highway electromechanical maintenance.

The fastest published infrared (IR) scene simulation called IMIRST by BAE Systems takes 32 minutes to construct a single 352 x 352 IR scene using a Pentium 4 (2.8GHz) dual core processor. In defence and homeland security, IR scene simulators are designed to test infrared missile approach warning systems (MAWS). A typical MAWS requires input scenes at the rate of over 50 scenes per second to effectively run key IR search and track algorithms. Current IR scene simulations fall well short in meeting this requirement. Advanced computing based on programmable logic provides a platform for parallel computing with complete freedom of choice about the granularity and structure of the parallel algorithm. In this paper we identify the key computationally intensive infrared algorithms and present a novel reconfigurable computing platform to accelerate infrared simulation.

Quantum key agreement (QKA) and quantum search algorithm (QSA) are two important branches in quantum topic. The former asks all participants to negotiate the agreement key equally, and none of them can fully determine the agreement key. The latter is used to provide an acceleration in searching the marked item in an unsorted database. To date, several QKA schemes based on either BB84 or entangled states have been proposed, the QKA protocol based on QSA is rare, only exists one multi-party QKA (MQKA) protocol based on QSA. In this paper, on the basis of the properties of Grover’s QSA, the first two-party quantum key agreement protocol based on quantum search algorithm known as Grover’s algorithm is proposed. The participants transmit a two-particle quantum state sequence directly by inserting decoy photons randomly. The initial-prepared two-particle quantum states are easy to be prepared with current technology. Moreover, there is no swapping entanglement technology used in this protocol, it only needs unitary operations and single-particle measurements. Compared with the existing two-party QKA protocol, the proposed QKA protocol is more efficient. In addition, the security analysis shows that the proposed protocol is secure against both external attacks and internal ones. Finally, the proposed protocol is generated to MQKA protocol based on quantum search algorithm.

How to tune the optimal basis size is one key problem for trajectory-basis based Non-rigid Structure from Motion (NRSFM). For the small basis size, the trajectory is poorly represented; however, for the too large basis size, the trajectory system becomes more ill-conditioned and the reconstruction error becomes unbounded. As an alternation of a lot of repeated experiments, an automatic selection method is proposed to determine the optimal basis size, which is implemented by analyzing frequency spectrum of reconstruction error. Moreover, trajectory basis selection is another key problem for trajectory basis based NRSFM, Discrete Cosine Transform (DCT) is introduced as the trajectory basis. Experiments on the real Pickup image sequences are designed to test the proposed method on selecting the optimal trajectory basis to recover 3D structure of non-rigid objects. Compared with the empirical value from multiple trials, the experimental results show that the proposed automatic selection method can improve both reconstruction accuracy and efficiency of NRSFM.

Automatic reference selection for quantitative EEG interpretation: Identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms

The volume measurement of various organs by single photon emission computed tomography (SPECT) has generally been performed by summing up the voxels lying within the outer edge of the organ determined by thresholding as a function of the maximum reconstructed counts within a volume of interest. The optimum threshold level, however, is influenced by various factors. To eliminate this problem, Mortelmans et al. (1986) proposed to use an automatic threshold selection method (ATSM) based on discriminant criteria and to correct the errors induced by the imperfect system response using the method based on linear regression analysis, which has some problems in practical application. We tried to use ATSM combined with a V filter. The correlation coefficient (r) and the regression equation between the true (x) and the calculated volume (y) obtained by experimentation using 22 phantoms with a volume ranging between 26 ml and 380 ml were as follows: r = 0.994 and y(ml) = 1.015 x +1.09 using ATSM combined with a V filter, while r = 0.993 and y(ml) = 1.110 x +30.55 using only ATSM. The relative error between the true and the calculated volumes decreased significantly (P less than 0.01) from 37.7% +/- 26.9% to 5.5% +/- 4.2%. The results suggest that ATSM combined with a V filter is useful for correcting the errors caused by an imperfect system response, and is available and reliable for SPECT volume determination even when the organ volume is small.

As, population increases use of World Wide Web goes on increasing for various purposes. People surfing websites for study purpose, education, entertainment point of view and also access websites for online shopping purpose. But required knowledge capturing is difficult and time consuming which is challenging one. So to overcome this challenge the proposed system gives a novel approach to provide a Web page recommendation. The proposed system consists of three knowledge based models. To improve performance in future key information extraction algorithm is used and comparison takes place between results obtained from applying only three knowledge based models and models along with key information extraction algorithm. Recommendation is given to page from weblog records. Experimental result shows that recommendation for webpage is better by using proposed system than existing system and execution time required for the proposed system is less as compared to existing system and accuracy of proposed system is more than existing system. This can be obtain by using key information extraction algorithm.

The objective of private authentication for Radio Frequency Identification (RFID) systems is to allow valid readers to explicitly authenticate their dominated tags without leaking tags' private information. To achieve this goal, RFID tags issue encrypted authentication messages to the RFID reader, and the reader searches the key space to locate the tags. Due to the lack of efficient key updating algorithms, previous schemes are vulnerable to many active attacks, especially the compromising attack In this paper, we propose a Strong and lightweight RFID Private Authentication protocol, SPA. By designing a novel key updating method, we achieve the forward secrecy in SPA with an efficient key search algorithm. We also show that, compared with existing designs, SPA is able to effectively defend against both passive and active attacks, including compromising attacks. Through proto-type implementation, we observe that SPA is practical and scalable in current RFID infrastructures. © 2007 IEEE.

The objective of private authentication for Radio Frequency Identification (RFID) systems is to allow valid readers to explicitly authenticate their dominated tags without leaking the private information of tags. In previous designs, the RFID tags issue encrypted authentication messages to the RFID reader, and the reader searches the key space to identify the tags. Without key updating, those schemes are vulnerable to many active attacks, especially the compromising attack. We propose a strong and lightweight RFID private authentication protocol, SPA. By designing a novel key updating method, we achieve the forward secrecy in SPA with an efficient key search algorithm. We also show that, compared with existing designs, (SPA) is able to effectively defend against both passive and active attacks, including compromising attacks. Through prototype implementation, we demonstrate that SPA is practical and scalable for current RFID infrastructures.

Recently, there has been an increase in the number of photos taken by people in especially unusual situations such as travel and ceremony. The photo groups taken in unusual situations have similar images and low-quality images such as under/overexposure and blurred images. For that reason, it is time consuming to summarize photos for creating photo albums. Therefore, we propose a method of automatic photo selection for photo albums. The proposed method can avoid to select low-quality images as candidate images automatically. Additionally, similar images are grouped and narrowed down the only most high-quality image that is selected as candidate image. The proposed method calculates the score of each candidate image, and selects final-images according to the score and scenes.

In many high dimensional problems, the dependence structure among the variables can be quite complex. An appropriate use of the regularization techniques coupled with other classical statistical methods can often improve estimation and prediction accuracy and facilitate model interpretation, by seeking a parsimonious model representation that involves only the subset of revelent variables. We propose two regularized stochastic regression approaches, for efficiently estimating certain sparse dependence structure in the data. We first consider a multivariate regression setting, in which the large number of responses and predictors may be associated through only a few channels/pathways and each of these associations may only involve a few responses and predictors. We propose a regularized reduced-rank regression approach, in which the model estimation and rank determination are conducted simultaneously and the resulting regularized estimator of the coefficient matrix admits a sparse singular value decomposition (SVD). Secondly, we consider model selection of subset autoregressive moving-average (ARMA) modelling, for which automatic selection methods do not directly apply because the innovation process is latent. We propose to identify the optimal subset ARMA model by fitting a penalized regression, e.g. adaptive Lasso, of the time series on its lags and the lags of the residuals from a long autoregression fitted to the time-series data, where the residuals serve as proxies for the innovations. Computation algorithms and regularization parameter selection methods for both proposed approaches are developed, and their properties are ex-

This paper presents an automatic selected channel method for improving brain-computer interface (BCI) performance. Finding an effective channel composition in a BCI system is important for parsing the complexity of these systems. In this study, several statistical methods based on an energy extraction method were used to automatically detect the composition of selected channels. We introduce four techniques for automatic channel selection to optimize the energy extraction method, such as mean, high mean, box, and high in box. The performance of all of the proposed techniques was evaluated based on performance accuracy, compression ratio, and channel mapping on a brain scalp. The performance of the proposed BCI framework was compared against a BCI framework that used a manual technique as the energy extraction method. The proposed BCI framework system used a conventional common spatial pattern (CSP) to extract features from two-class motor imagery EEG signals before employing extreme learning machine (ELM) to classify the features of the EEG signal. As a result, the proposed automatic channel selection methods were found effective in finding optimal channels and provided better performance accuracy. In general, the proposed method improved the conventional BCI performance by up to 16% accuracy and 87% channel compression size. Besides that, the automatic technique also yielded better BCI performance accuracy of up to 5% compared to the BCI system that used the manual technique of energy extraction as its EEG channel selection method.

Because of the presence of cloud, the quality and application of data obtained in visible light and infrared bands are affected when remote sensing images are generated. This paper proposes an automatic selection method with the best cut-off frequency to remove the effect of thin cloud in remote sensing images quickly and efficiently. Based on the homomorphic filtering method of the simple thin cloud imaging model and the self-similarity of the spatial form of thin cloud and haze, this paper uses the multi-fractal technology and the filtering technology of the S-A model (power spectrum-area model) to determine the filtering radius automatically. The experiments of removing thin cloud and haze for remote sensing images show that the filtering technology of the S-A model is related to the spatial form of image contents directly. Compared with usual filtering technologies which choose frequency as radius, this method can not only achieve rapid and automatic filtering of remote sensing images, but also remove the effect of thin cloud in remote sensing images effectively.

Landmarks are essential in non-rigid shape registration for identifying the correspondence between designs and actual products. In 3D printing, manual selection of landmarks becomes labor-intensive, due to complex product geometries and their non-uniform shape deviations. Automatic selection, however, has to pinpoint landmarks indicative of geometric regions prone to deviations for accuracy qualification. Existing automatic landmarking methods often generate clustered and redundant landmarks for prominent features with high curvatures, compromising the balance between global and local registration errors. To address these issues, we propose an automatic landmark selection method through deviation-aware segmentation and landmarking. As opposed to segmentation for semantic feature identification, deviation-aware segmentation partitions a freeform product for high-curvature region identification. Prone to deviation, these regions are generated through curvature-sensitive remeshing to extract vertices of high curvature and automatic clustering of vertices based on vertex density. Within each segment or high-curvature region, a curvature-weighted function is tailored for the Gaussian process landmarking to sequentially select landmarks with the highest local curvatures. Furthermore, we propose a new evaluation criterion to assess the effectiveness of selected landmarks through registration. The proposed approach is tested through automatic landmarking of printed dental models.

<p>The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Additionally, the process of calculating interferograms itself directly affects the accuracy of the SBAS-InSAR measurements, whereby the selection of high-quality interferogram pairs is crucial for SBAS data processing. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. However, there are some methods including simulated annealing (SA) searching strategy, the graph theory (GT) and others. Until now, the most effective approach of high-quality interferogram selection still relies on the traditional manual method. Due to the high degree of human interaction and a large risk of repetitive work, this traditional manual method increases the instability and inconsistency of the deformation calculation.<br>Considering that the different qualities of interference pairs show different color characteristics, the DCNN method is adopted in this study. The ResNet50 model (one of DCNN models) has the advantages of representing a standard network structure and easy programming. The idea is based on the fact that interferograms less contaminated by different noise sources display smaller color phase changes within a certain phase range. Hence, training sets containing almost 3000 interferograms obtained from land subsidences in several subregions of Shenzhen in China with varying contaminations of noise were established. Up next, the ResNet50–DCNN model was set up, the respective parameters were determined through analysis of the data sets trained, and traditional interferogram selection methods were used to evaluate the performance. For simulation experiments and the evaluation and validation of real data, phase unwrapping interferograms obtained by the time-spatial baseline threshold method are used to classify high and low quality interferograms based on the ResNet50 model. The quantity of high quality interferograms extracted by the ResNet50–DCNN method is above 90% for the simulation experiment and above 87% concerning the real data experiment, which reflects the accuracy and reliability of the proposed method. A comparison of the overall surface subsidence rates and the deformation information of local PS points reveals little difference between the land subsidence rates obtained by the ResNet50–DCNN method and the actual simulations or the manual method. <br>The proposed advanced method provides an automatized and fast interferogram selection process for high quality data, which contributes significantly to the application of SBAS-InSAR engineering. For future research, we will expand the training samples and study DCNN models to further improve the general accuracy for a wider applicability of this method.</p>